Connecting Industry & Government to Accelerate Innovation in Engineering and Manufacturing through Computational Design, AI & Machine Learning
CD/DC continues the CDFAM Computational Design Symposium program previously held in NYC, Berlin, Amsterdam and Barcelona, bringing together the most innovative designers, engineers and architects working across computational design, artificial intelligence, and data-driven engineering.
The event is an in-person forum focused on practical methods, real-world applications, and cross-disciplinary exchange.
- LOCATION: Station DC, 1323 4th St NE, Washington, DC
- DATES: July 15-16, 2026
- Early Bird Registration Now Open
In Washington, DC, the program will be extended to include additional presentations and roundtable discussions aligned with U.S. government initiatives.
These sessions are intended to create additional points of engagement between industry, academia, and public-sector stakeholders aligned with the core focus of the CDFAM program.
Free registration for government employees for evening sessions. Registration and full event details below.
Purpose
CD/DC convenes the ecosystem shaping the future of how things are designed, engineered, and delivered at scale and speed.
- Government: Align policy, funding, and infrastructure with emerging design and manufacturing capabilities.
- Suppliers: Integrate at the design phase to enable fast, flexible fulfillment.
- Engineering Leaders: Build system-level solutions with simulation-integrated, AI-enhanced workflows.
- Software Developers: Scale impact by embedding tools at the point of design, not just execution.
Focus Areas
- System-Level Design Integration
From materials to architecture , enabling vertical interoperability across design, simulation, and production. - Generative + Simulation-Driven Engineering
Support decision-making across performance, cost, and manufacturability from the earliest phases of development. - Connected Digital Supply Chains
Feed validated, production-ready data to suppliers to reduce iteration cycles and accelerate delivery. - Agile Manufacturing Enablement
Leverage flexible manufacturing platforms through up-front computational workflows and standards-based data exchange. - Public-Private Infrastructure for Innovation
Establish common frameworks to support scalable, cross-sector collaboration between industry and government.
Who Should Join
- Engineering organizations deploying simulation-integrated workflows across product development.
- Software platforms enabling generative design, simulation, AI and machine learning augmented, and system modeling.
- Government agencies supporting industrial strategy, defense, energy, infrastructure, or sustainability.
- OEMs and suppliers building modular, responsive supply chains.
- National labs and research institutions focused on digital thread, design science, and model-based systems engineering.
Register to Attend
Standard Registration
$1250
Until July 15th
EXTENDED
Early Bird Registration
$850
Extended until June 15th, or Sold Out
Academic Registration
$550
Until July 1st
Team Registration
$650 each
Bring a team, minimum of four attendees, for $650 each plus transaction fees.
Until July 10th
PROGRAM
Currently Under Development & Subject to Change
Organization:
CDFAM
Presenter:
Duann Scott
Welcome to CD/DC
Presentation Abstract
Welcome to CDFAM Washington D.C. opening remarks giving context to the event and it’s program.
Speaker Bio
Duann Scott is the founder of the CDFAM – Computational Design Symposium, bringing together experts across design, engineering, and software development to explore the future of computational design at all scales.
He is also the Executive Director of the 3MF Consortium, driving the development of an open data standard for additive manufacturing.
With a background spanning industry, academia, and software, Duann focuses on advancing innovation in digital design and advanced manufacturing.
Keynote Presentation
Organization:
NASA Goddard
Presenter:
Ryan McClelland
Text to Spaceship: Accelerating Mission Development with AI
Presentation Abstract
AI is transforming how we design and build space missions. At NASA, we’ve already shown that AI can take requirements and rapidly generate optimized structures that are lighter, stronger, and delivered in days instead of months. The Text-to-Spaceship vision scales this up through a secure, cloud-deployed ecosystem of AI-accessible design, analysis, and manufacturing tools. Language-defined requirements flow through these automated systems, accelerating mission development by an order of magnitude. In this talk, I’ll share how we’ve gone from balloon brackets to full payload designs and why Text-to-Spaceship is becoming a near-term reality that will redefine how we explore the universe.
Read the interview with Ryan McClelland
Speaker Bio
From a young age, Ryan McClelland has been captivated by futurism and technology, aspiring to contribute to a brighter future. As a Research Engineer in NASA GSFC’s Instrument Systems and Technology Division, he pursues developing and implementing digital engineering technologies for space-flight missions. Ryan is particularly excited about the potential of Artificial Intelligence, Virtual Reality, Generative Design, and Digital Manufacturing to accelerate space systems development.
Ryan’s work using AI to develop spaceflight structures has been featured by NBC News, The New York Times, Dezeen, Popular Science, and Aviation Week. He was recently named to the Fast Company 20 in AI list. In addition to his research, Ryan has played a significant role in various flight missions, including designs currently on orbit aboard the Hubble Space Telescope and International Space Station.
Keynote Presentation
Organization:
Boston Dynamics
Presenter:
Brian Ringley
Authoring Autonomy
Presentation Abstract
When the core value proposition of the humanoid form is generalized capability through massive retaskability, is the world’s most dynamic humanoid hardware enough? The next industrial revolution will rely on industry-leading AI brains in addition to production-grade humanoids and scaled manufacturing. These brains are taught with large data sets rather than programmed, but spatial reasoning can’t be scraped from the internet like text and language (yet). It demands a novel ecosystem of agentic software sandboxes where users can teach robots through physical demonstration, naturally describe tasks, and interactively refine generative robot behaviors and application interfaces.
Speaker Bio
Organization:
Atomic Machines
Presenter:
Marta D’Elia
Minimizing the thought-to-thing time at Atomic Machines
Presentation Abstract
Atomic Machines is developing a completely new AI-native digital micro-manufacturing technology stack that will enable new classes of microdevices which we call MEMS 2.0. Critical to the mission is minimizing “thought-to-thing” time, i.e., the latency between an idea and a manufacturable device. Rather than maximize AI usage, we pursue a generate-to-solve strategy that uses as little AI as necessary, using AI to blend models grounded in physics, constraints, and verification to ensure our designs are trustworthy by construction.
In this talk we present an end-to-end workflow that converts an informal description into a precise set of engineering requirements. From this input, the system generates a multi-physics simulation of a first candidate device drawn from a catalog, and closes the loop modifying it with a manufacturability-first design optimization approach. We automatically move from informal descriptions to producing parts that meet performance targets while satisfying process constraints, tolerances, and assembly requirements.
We demonstrate our “Matter Design Engine” on a single mechanical component in an electromagnetic actuator, showing requirement formalization, simulation instantiation, and generation of manufacturable designs evaluated against acceptance tests.
Speaker Bio
Marta D’Elia is the Director of AI and Modeling & Simulation at Atomic Machines and an Adjunct Professor at Stanford University’s Institute for Computational and Mathematical Engineering. Her work focuses on scientific machine learning, physics-based simulation, and data-driven modeling for complex multiscale systems. She develops AI-enabled modeling approaches that integrate numerical simulation, machine learning, and uncertainty quantification to accelerate engineering design and manufacturing innovation. Prior to Atomic Machines, she held research and technical leadership roles at Meta, Pasteur Labs, and Sandia National Laboratories.
Organization:
General Atomics Aeronautical Systems
Presenter:
Brandon DeMille
Computational Design in Aerostructures: Topology Optimization for Conceptual Design and Trade Studies
Presentation Abstract
Computational design methodologies, including topology optimization, are transforming airframe structures development by enabling rapid exploration of design configurations during early conceptual phases. This presentation demonstrates a workflow that enables informed decision-making across disciplines and accelerates the path from initial concept to detailed design. A fuselage case study illustrates the simultaneous optimization of composite laminates for skins, substructure geometry, and overall shaping. This integrated approach facilitates quantitative trade-offs among competing priorities such as cost, structural performance, manufacturability, and production rate.
Speaker Bio
Brandon DeMille has spent nearly two decades bridging the gap between computational design theory and real-world manufacturing constraints. As a Senior Staff Engineer in General Atomics Aeronautical Systems’ Advanced Manufacturing Technology group, he’s a recognized expert in topology optimization methods that help teams explore new concepts for aerostructures while keeping manufacturability front and center.
Before joining GA-ASI, Brandon led an R&D group at Callaway Golf, where his work on high-rate composite manufacturing processes and topology-optimized structures earned multiple industry awards and a wide range of patents. Brandon brings deep technical expertise and hands-on experience taking concepts from optimization results to finished hardware. He holds degrees in Physics, Economics, and Mechanical Engineering from the University of California at Santa Barbara.
The Era of Living Machines: How Biology Will Build the Next Generation of Building Materials
Presentation Abstract
What if material fabrication could shift from assembly to growth—and be directed with precision through external fields?
This work introduces magnetotropic plants: genetically engineered organisms in which gravity-sensing organelles (statoliths) are rendered magnetically responsive. By replacing gravitational cues with externally applied magnetic fields, plant growth direction can be actively controlled in real time. This enables programmable morphogenesis, where biological growth becomes a steerable process rather than a fixed outcome of genetics and environment.
The presentation will outline the biological mechanism, the experimental framework, and the implications of this approach for material production.
Magnetic fields act as an invisible, non-contact control layer, allowing spatial and temporal guidance of growth without mechanical intervention.
Beyond applications in microgravity environments such as space, this work suggests a broader shift in how we produce materials—moving from extractive, energy-intensive processes toward growth-driven fabrication, where form emerges from the interaction between engineered biology and designed environmental conditions.
Speaker Bio
Giorgia Cannici is an Assistant Professor at Virginia Tech School of Architecture, working at the intersection of bioengineering and advanced manufacturing. Her research focuses on engineering living materials—programming organisms to produce and organize matter for architectural applications.
Trained across biology, engineering, and architecture, she holds a degree in Biology from Harvard University, a degree in Biomedical Engineering from Tufts University, and a PhD in Biological Systems Engineering from Virginia Tech. She is also a registered architect in Europe. Prior to academia, she worked at leading international architecture firms including Zaha Hadid Architects, Foster + Partners, and UNStudio.
Her work explores how biological processes can be harnessed as fabrication systems, enabling new approaches to sustainable, high-performance materials for the built environment.
Organization:
nTop
Presenter:
Jan Vandenbrande
Fast and Robust Design with Implicit Functions and Direct Simulation
Presentation Abstract
Current Computer Aided Design systems excel in static detailed design but are too fragile and slow to support Design Exploration and Multidisciplinary Design Optimization for conceptual and preliminary design. This talk introduces a new approach to modeling products that overcomes these shortcomings based on implicit functions popularized in the animation industry. The main benefits of the approach is that is responsive to the need to design and redesign products in days or weeks and not month or years because of absolute robustness to parametric change minimizing human intervation; lightning fast evaluations leveraging GPUs; and performing analysis directly from the representation w/o the need of human intervention to generate cumbersome and error prone meshes.
Speaker Bio
Dr. Jan Vandenbrande is currently the VP for Aerospace and Defense for nTop and advisor to DARPA focusing on computational design and manufacturing. He previously served as Vice President of SRI International’s Future Concepts Division (formerly Xerox PARC), driving disruptive innovation in advanced production, clean technologies, and intelligent systems for societal and economic impact. Earlier, as a DARPA Program Manager in the Defense Sciences Office, he initiated and funded transformative research in design, manufacturing, materials, and mathematics to deliver breakthrough capabilities for future DoD platforms and the public good.
Before joining DARPA he was a Technical Fellow and Senior Manager of the Applied Math group at Boeing where directed the development of in house computational tools that enabled breakthrough design and manufacturing processes that improved Boeing’s products. He was the original author of GEODUCK, an advanced geometry and math system to enable Multi-disciplinary Design Optimization, a system still in use after 25 years.
At Unigraphics, now Siemens NX, Dr. Vandenbrande developed the initial architecture and user interface for the next generation Computer Aided Manufacturing (CAM) system and improved the system’s computational performance and accuracy. He is a former associate editor of JCISE, AIEDAM and CAD. He received his Ph.D. in Electrical Engineering from the University of Rochester and an engineering degree from the Vrije Universiteit van Brussel, Belgium. Jan is a frequent invited keynote speakers, published eight papers, presented at numerous conferences and holds five patents.
Organization:
Istari Digital
Presenter:
Rebeka Melber
The Digital Thread in the Real World: Multiple Partners, Multiple Tools, One Truth
Presentation Abstract
As the Department of Defense accelerates adoption of digital engineering and advanced manufacturing, the challenge is no longer defining the digital thread—it is executing it across a fragmented Defense Industrial Base (DIB). This session will explore a consortium-based approach to demonstrating an end-to-end digital thread spanning design, build, operations, and sustainment—executed within each partner’s native environment.
Rather than forcing tool or data standardization, this effort enables participating organizations to use their own systems, data architectures, and processes while securely sharing only what is necessary to maintain a federated, authoritative source of truth. The result is a practical model for interoperability that reflects real-world constraints: multiple vendors, distributed ownership, and varying levels of digital maturity.
Speaker Bio
Rebeka “Cam” Melber is the Director of Programs at Istari, where she oversees program execution and works closely with government and industry teams adopting next-generation engineering capabilities. Her career has focused on helping complex defense programs transition to modern digital infrastructure and accelerating the adoption of new technologies within sensitive mission environments. Drawing on experience across the Department of Defense, her work centers on making advanced engineering tools accessible to the people who need them most. Rebeka has led several first-of-their-kind capabilities and brings a blend of strategic vision, engineering discipline, and operational execution to advancing how complex systems are designed and delivered.
Organization:
Arena Physica
Presenter:
Pratap Ranade
Artificial Intuition: Building an AI Mind for Electromagnetic Design and Engineering
Presentation Abstract
Most advances in computational design focus on mechanical structure — domains we can visualize and have evolved an intuition for. But as modern hardware becomes increasingly software defined, the unseen and unintuitive world of electromagnetism is taking center stage.
Conventional solvers can simulate fields, yet they cannot imagine new ones. Over the past year, our team has been building toward that capability. At CDFAM NYC and Barcelona, we shared early results from Atlas — an AI that learns electromagnetic behavior inductively from test data rather than deductively from first principles, enabling verification, optimization, and design postulation in domains where classical simulation reaches its limits.
Drawing on experiments like the Kondo mirage, where physical discovery outpaced simulation, Atlas was developed to close that gap. With the release of Atlas RF Studio now in public beta, that work is moving from research demonstration into practitioner hands for the first time.
This talk presents results from real-world applications in semiconductors and aerospace, and outlines what the beta program reveals about where AI-driven electromagnetic design is heading.
Speaker Bio
Organization:
C-Infinity
Presenter:
Sai Nelaturi
AI-Enabled Assembly Configuration Spaces: Encoding Mechanical Intuition at the Design-Manufacturing Interface
Presentation Abstract
The gap between digital design and physical assembly is not primarily a geometry problem. It is a reasoning problem. Engineering teams spend thousands of hours communicating assembly intent through manual CAD workflows, managing configuration complexity across product variants, and catching fitment and feasibility issues that could have been identified before any metal was cut. The cost is measured in weeks of engineering time per product release cycle.
C-Infinity is building foundational AI that reasons about three-dimensional geometry, motion, spatial constraints, and production logic — not as a search over predefined templates, but as genuine mechanical inference. AutoAssembler connects directly to existing CAD and PLM environments to automate process planning, generate virtual builds, and accelerate engineering change order reviews, compressing manual workflows from weeks to minutes.
This presentation covers the technical architecture behind assembly configuration space reasoning, the challenge of encoding mechanical intuition in a form that generalizes across product types and manufacturing contexts, and what it means to treat assembly planning as an AI problem rather than a CAD problem.
Speaker Bio
Ph.D. Mechanical Engineering, UW-Madison. Expert in CAD, AI, and Digital Manufacturing. Former R&D Director at Carbon and PARC. DARPA and UW career award recipient.
Physics AI as a Strategic Advantage: How Physics-based AI Models Are Reshaping U.S. Defense Engineering
Presentation Abstract
The next decade of great-power competition will be won or lost in the engineering loop. Adversaries have increased the speed of iteration in hypersonics, undersea platforms, and autonomous aircraft and the U.S. defense industrial base cannot keep up by relying on traditional engineering workflows. Closing that gap requires a step-change in how programs are developed with AI-accelerated engineering.
In this talk, we will introduce Physics AI as a strategic capability for the Department of War and the United States Intelligence Community. Luminary’s SHIFT models built with Physics AI deliver near-real-time, high-fidelity inference that provides accurate estimations of performance, including field predictions, across geometries and operating regimes, while remaining anchored to first-principles physics.
We will introduce three mission-relevant models: SHIFT-Missile for rapid aerodynamic design across subsonic to hypersonic regimes; SHIFT-Submarine for hydrodynamic and undersea dominance; SHIFT-MaxQ for the affordability-driven design of Collaborative Combat Aircraft and autonomous wingmen; and SHIFT-Rocket for a space launch vehicle. Physics AI is becoming table stakes for the next generation of U.S. military systems, and the organizations that adopt it first will set the pace.
Speaker Bio
Chief Technical Officer & co-founder of Luminary. Reimagining high-performance computing.
Professor in Aeronautics & Astronautics with specialization in high-fidelity multi-disciplinary analysis, design, and optimization. Other areas of specialty include applied aerodynamics, high-performance parallel computing, aeroelasticity, turbomachinery computations, and aircraft design.
Git for Hardware: Version Control as the Foundation for Agile Systems Engineering
Presentation Abstract
Software development was transformed when version control became infrastructure rather than afterthought. Hardware engineering has not had an equivalent transition. Requirements live in documents. System models are stored in vendor-locked platforms. Design reviews happen in meetings rather than pull requests. The result is programs that cannot iterate at the speed the environment demands.
SysGit applies the tools and workflows that scaled software development — branching, merging, diffing, CI/CD pipelines — directly to systems engineering artifacts, built on SysMLv2 and backed by existing Git infrastructure. Requirements, system models, and verification activities are captured in a single machine-readable format, traceable across the full program lifecycle and accessible to every stakeholder from specialist engineers to contracting officers.
This presentation covers the technical architecture behind that approach, the role of agentic AI in automating requirements generation and model validation, and what continuous acquisition looks like when the digital thread is built on open standards rather than walled gardens.
Speaker Bio
Co-founder and CEO of SysGit, We build software supporting a DevOps for Hardware approach for complex engineering, with a focus on the messy human layer of engineering: Requirements Management and MBSE.
Prior to founding SysGit, I supported the launch of over two dozen spacecraft to orbit during my time at SpaceX and was Mission Manager for Falcon 9 flight 20, ORBCOMM-2, the first booster to successfully return to land. I also lead the development of Slingshot Aerospace’s Edge platform, performing sensor fusion across visible light, IR, and RF sensors to provide tactically relevant and actionable insights to the warfighter on remote platforms.
I’m passionate about digital engineering and treating hardware iteration like software iteration, enabling teams to field new world-changing systems faster than their competitors and adversaries.
Optimal Lattice Selection for PCM Thermal Management
Presentation Abstract
PCMs provide cooling without requiring an extra power source, unlike fans or active liquid cooling systems. They absorb peak energy loads during operation and release that heat when the ambient temperature drops, acting as a buffer against rapid temperature changes. This allows for more compact thermal management systems. Because most PCMs have inherently low thermal conductivity, they often fail to absorb or release heat quickly enough for high-demand applications. Designers can significantly improve the thermal performance of Phase Change Materials (PCM) by embedding lattice structures. Embedding a highly conductive lattice, such as aluminum or copper, forms a “thermal skeleton” that functions as a heat highway, dissipating heat more quickly and evenly through the PCM.
Adding a 3D-printed metal lattice can increase the effective thermal conductivity of a PCM system by an order of magnitude compared to pure PCM. The internal structure provides a continuous path for heat conduction, which can double the melting speed. Beyond thermal benefits, the lattice provides mechanical support to the PCM, preventing leakage and helping it maintain its shape during the liquid phase.
Simulating PCMs’ thermal behavior is difficult due to the highly nonlinear nature of latent heat release, the shifting phase-change boundaries, and the significant differences in physical properties between the solid and liquid states. This presentation demonstrates simulation techniques and showcases the process of optimal lattice selection.
Speaker Bio
Dr. Andreas Vlahinos is the Chief Technology Officer at Advanced Engineering Solutions. His expertise includes Design for Additive Manufacturing (DfAM), Computer-Aided Innovation, Generative Design, Lattice Structures, and Simple Solutions to Complex Problems. He has significantly contributed to the rapid development of products by implementing Computer-Aided Engineering across various government agencies, including NASA, NREL, SANDIA, DOE, NCDMM, and US Army Aviation & Missile Command, as well as industry partners such as SpaceX, Lockheed Martin, General Dynamics, BAE, United Launch Alliance, Rafael Defense Systems, NAVISTAR Defense, IBM, Coors, Alcoa, Allison Engine Comp., Solar Turbines, Ball, American Standard, Kohler, Varian, Stewart & Stevenson, Harris Corp., TDM, PTC, MDI, Ford Motor Company, Rockwell Collins, BIC, BAE Systems, XEROX, Woodward Inc., Gichner Shelter Systems, NAVISTAR Defense, Flyer Defense, Viper Inc., Lincoln Composites, Advanced Composite Products & Technology, Inc., TetraPak, and others.
Dr. Vlahinos has also served as a Professor of Structural Engineering at the University of Colorado, receiving the Professor of the Year Award multiple times. He has been honored with the R&D 100 award and holds several patents. He earned his Ph.D. in Engineering Science and Mechanics from the Georgia Institute of Technology.
Dr. Vlahinos is a renowned keynote speaker and panelist at international conferences on various subjects, including Generative Design, Innovation, and DfAM.
Bridging the CAD-to-Simulation Gap: Integrating Meshing-free Isogeometric Analysis into Industrial Workflows
Presentation Abstract
The traditional “design-to-analysis” loop is often bottlenecked by the laborious process of mesh generation, which can consume up to 80% of total simulation time. This presentation introduces Coreform IGA for Abaqus, a solution that brings CAD-exact isogeometric analysis (IGA) directly into the Abaqus ecosystem. By utilizing Coreform’s Isogeometric Analysis approach, users can perform high-fidelity simulations directly on CAD geometry, effectively bypassing the need for traditional finite element meshing.
Speaker Bio
Matthew Sederberg, CEO of Coreform, has spent over two decades innovating at the intersection of geometry and physics. His work focuses on eliminating the “mesh bottleneck” through the commercialization of isogeometric analysis (IGA). Following the acquisition of his first company, T-Splines, by Autodesk, Matthew has focused on building Coreform to commercialize CAD-based simulation. He is passionate about creating workflows where geometry remains exact from the first sketch to the final simulation, a mission he continues to lead through Coreform’s integration with established ecosystems like Abaqus.
From Text to Robotic Assembly: 3D Generative AI and Discrete Robotic Assembly for Making Physical Objects
Presentation Abstract
Recent advances in 3D generative AI make it possible to create object geometries directly from natural language, but turning these digital forms into functional physical objects remains a major challenge. Most generated 3D models are meshes that do not contain the component level, structural, material, and assembly information required for robotic fabrication. This presentation introduces a research pipeline that combines 3D generative AI, vision language models, and robotic assembly to transform text prompts into multicomponent physical objects. Rather than only asking what an object should look like, the system reasons about how it should be physically composed, including where stronger, lighter, stiffer, or more flexible components are needed. The work points toward a future in which AI driven design systems can generate not only visual form, but also buildable, reusable, and materially informed assemblies for real world fabrication.
Speaker Bio
Alexander is a researcher working at the intersection of artificial intelligence, augmented reality, robotics, design, and fabrication. He is developing systems and products that enable natural interactions between humans, machines, and the world around us. Alexander’s research contributions have been published in Neural Information Processing Systems (NeurIPS), the Association for Computing Machinery (ACM) Conference on Human Factors in Computing Systems (CHI), Tangible Embedded and Embodied Interaction (TEI), Symposium on Computational Fabrication (SCF), Automation in Construction (AutCon), Architectural Intelligence (ARI), the International Conference on Computational Design and Robotic Fabrication (CDRF), Education and Research in Computer-Aided Architectural Design in Europe (eCAADe), and the Association for Computer-Aided Design in Architecture (ACADIA).
Currently, he is a graduate student at MIT in the Department of Electrical Engineering and Computer Science (EECS) and the Department of Architecture. At MIT, Alexander works with the Computer Science and Artificial Intelligence Lab (CSAIL) and the Center for Bits and Atoms. He completed his Bachelor of Architecture degree from Cornell University, where he received a minor in Computer Information Science. At Cornell University, Alexander held research positions in the Robotic Construction Lab and the Sabin Lab. His professional experience includes work with Google Research, Microsoft Research, Autodesk Research, Skidmore Owings & Merrill (SOM), BRIC Architecture, and Proximity Design.
Alexander’s design and research have garnered recognition from the American Institute of Architects (AIA), the Museum of Craft and Design, Google, Amazon, and the United Nations. Before pursuing his Bachelor’s degree in the United States, Alexander was raised in Yangon, Myanmar. Previously, he was a Steve Jobs Archive Fellow and a Morningside Academy of Design Fellow.
Organization:
InfinitForm
Presenter:
Dr. Michael Bogomolny
Requirements to Production Part in Minutes: How Physical AI Closes the Loop Between Optimization and Manufacturing
Presentation Abstract
The gap between optimized geometry and manufacturable components has been the defining constraint of computational design for three decades. Topology optimization produces brilliant forms that machinists cannot cut. Those forms are not editable in CAD / or CAD friendly. Simulation validates performance that mainstream manufacturing cannot reproduce. The result: design cycles measured in months, not days, and engineering organizations forced to choose between what is optimal and what is buildable.
InfinitForm was built to eliminate that tradeoff. The platform takes geometrical, engineering, manufacturing and cost constraints as input and outputs production-ready parametric CAD geometry, optimized simultaneously for structural performance and the specific manufacturing process it will be produced with, whether CNC machining, additive manufacturing, casting, extrusion, or injection molding. Every output carries full design history, constrained sketches, and parametric relationships, making it immediately editable in the CAD environment the engineering team already uses. GPU-accelerated solvers and optimizer, the system compresses what previously required weeks of iteration into minutes of compute.
This talk presents the technical architecture behind that capability, the manufacturing constraint modeling approach that makes outputs buildable rather than merely optimal, and results from production deployments at aerospace, defense, and advanced manufacturing organizations. It examines what changes when design for performance and design for manufacturing are solved as a single problem rather than sequential steps, and what that means for the engineering organizations, defense programs, and industrial supply chains now entering the Physical AI era.
Speaker Bio
Dr. Michael Bogomolny is an industry leader in AI-driven design optimization and advanced manufacturing with over two decades of experience in the engineering software field. As the Founder and CEO of Infinitform, Michael is driving innovation in design processes by seamlessly integrating manufacturing and performance criteria at the earliest stages, delivering faster, more efficient, and production-ready designs.
Prior to founding Infinitform, Michael co-founded ParaMatters, a leading generative design software platform for additive manufacturing, later acquired by Carbon. He also served in senior engineering roles at Hyperloop and Altair Engineering, where he developed cutting-edge solutions in structural optimization.
Recognized globally for his expertise in structural and multidisciplinary optimization, computational geometry, and CAD/CAE, Dr. Bogomolny has authored more than 25 scientific peer-review publications. He holds a Ph.D. from Technion – Israel Institute of Technology and completed postdoctoral research with the prestigious TopOpt group at the Technical University of Denmark.
Organization:
Not a Robot Engineering
Presenter:
Matthew Shomper
From Horns to Armor: Biomimicry, Computational Design, and the Future of Impact Protection
Presentation Abstract
Nature has been solving the problem of impact protection for millennia, in order to arrive at solutions far more elegant than anything on the market today. The microstructure of a bighorn sheep’s horn is one of the most striking examples : a geometry that is brutally efficient at scattering and absorbing energy, such that the animal can sustain repeated high-speed collisions without lasting damage. The challenge has always been translating that geometry into something we can actually manufacture.
Additive manufacturing allows us to build internal geometries that were previously impossible to fabricate : graded densities, interlocking fiber patterns, and layered structures that mirror what nature spent millions of years optimizing. By digitally modeling the ram’s horn at the microstructural level and translating those patterns directly into printable designs, we can produce armor components that outperform conventional materials in energy absorption while perfectly conforming to the body.
This work represents a broader shift in protective equipment design: away from material selection alone, and toward architecture as the primary engineering tool.
Speaker Bio
Matthew is a visionary leader in the computational design of advanced 3D-printed medical implants, with 15 years of experience in engineering, research, and innovation. As an inventor, creator, and passionate leader, he has been a part of founding businesses focused on additive manufacturing and is an internationally recognized speaker on biomimicry, computational modeling, and additive manufacturing – lecturing at conferences and prestigious universities including MIT and Harvard. Matthew’s work is driven by his passion for exploring the macro and micro of biological forms, turning algorithms into functional structures for physical devices. He has pioneered the idea of a “biologically advantageous implant,” and has also spearheaded multiple public initiatives to synthesize biological structures as computational models for use in engineered products. He currently is the founder and principal consultant of Not a Robot Engineering, a co-founder of LatticeRobot, and involved in several other stealth startups.
From Simulation to Spatial Reasoning: Training AI in Synthetic Worlds to Interpret Real Ones
Presentation Abstract
Simulation is core to computational design but it’s also how we train AI to perceive and reason about the physical world. This talk walks through Lexset’s pipeline from synthetic data generation to deployed spatial intelligence. We use procedural 3D environments and physics-based sensor emulation across EO, IR, thermal and SAR to train vision models entirely in simulation. Those models feed into an agentic AI system that tracks objects in 3D space, reasons over geospatial data, makes decisions based on spatial context and sends structured reports to operators via TAK. Everything runs at the edge. The talk covers how simulation tools familiar to this community can go beyond engineering physical products to generating training worlds and spatial reasoning for autonomous AI in real environments.
Speaker Bio
Agentic Engineering: Generative AI in structural applications
Presentation Abstract
Over the past decade, CORE studio at Thornton Tomasetti has developed a vast array of classical Machine Learning tools for structural design and analysis, which have proven incredibly helpful for rapid iteration in the early stages of a project. The very same ML tools that help our engineers work more productively are now being utilized by agentic systems that orchestrate and execute complex, multi-stage design workflows. This presentation will explore in depth CORE studio’s recent experiments in integrating agentic AI at an enterprise level, along with the safety, security, and compliance concerns such integration entails. We will also examine our work in multi-agent collaboration using the A2A protocol, as well as my personal efforts at bridging the gap between LLMs and CAD software using MCP. These topics are all part of a larger discussion about the evolving relationship between engineers and their tools, and the shifting role of a designer in the early days of the Intelligence Age.
Speaker Bio
Sergey Pigach is a Senior Associate Applications Engineer at CORE studio | Thornton Tomasetti. Sergey’s work builds on his architectural training by bridging the domains of technology and design, driving him to develop computational tools for architects, designers, and engineers. Since joining CORE studio he has worked on desktop and web-based projects including Swarm, a cloud compute solution for Grasshopper; ShapeDiver, a desktop client integration following a merger; and—most recently—Cortex, CORE Studio’s new MLOps platform.
From Requirements to Manufacturable Systems: Agentic AI on a Live Engineering Knowledge Graph
Presentation Abstract
Most ‘AI for engineering’ tools today sit beside the design process with a chat window next to a CAD viewer, a copilot that summarizes documents someone still has to act on. The harder problem is putting AI inside the loop, where it can read and write the same structured representation of the system that engineers, simulations, and downstream manufacturing all depend on.
This talk covers how we approached that problem at Celedon Solutions while building Davinci, an engineering platform where agentic AI operates directly on a live knowledge graph of the system under design. Requirements, components, interfaces, behaviors, and their relationships all live in one connected structure, and the agents that work on it can pull structured model content out of reference documents, generate and compare architectural alternatives against performance and cost constraints, trace requirements through simulation results, and reach into external tools such as parts databases, Python simulations, PLM systems through APIs and Model Context Protocol.
I’ll walk through the design decisions behind a graph-native rather than document-native foundation, show where generative exploration has meaningfully compressed early-phase trade studies in aerospace and defense pilots, and talk honestly about the failure modes where agents confidently produce plausible-looking nonsense, and what guardrails and iteration strategies actually work. The goal is a practical view of what it takes to move AI from the margins of the engineering workflow into the part of the process where design decisions actually get made.
Speaker Bio
Chris Helmerich is CTO of Celedon Solutions, where he leads development of Davinci, an AI-native platform for systems engineering and design automation. His background spans machine learning and engineering software across NASA, Jacobs, and ExoAnalytic Solutions, where he worked on anomaly detection models, software testing tooling, and convolutional neural networks for satellite detection and classification. At Celedon, his focus is on the systems problems that sit between generative AI and real engineering work: knowledge representation, agent architecture, and keeping the digital thread intact from concept through manufacturing.
Physics and Geometry Aware Generative AI for Engineering
Presentation Abstract
Modern product development increasingly requires simultaneous optimization across multiple physics domains, from fluid dynamics and thermal management to structural mechanics and electromagnetics. The complexity of these multi-physics design spaces has grown beyond what traditional CAD-driven workflows and sequential simulation pipelines can efficiently address. Engineering teams face unprecedented challenges as products now span multiple physics and domains, while the number of viable design options far exceeds what can be manually explored within typical development timelines.
Generic large language models (LLMs), while powerful in natural language tasks, lack the spatial and physical reasoning required to generate meaningful engineering geometry or reliably predict performance outcomes across these domains. Traditional parametric optimization approaches, constrained by predefined templates and limited design space exploration, result in sub-optimal concepts that fail to leverage the full potential of modern computational capabilities. The fragmented nature of conventional design processes, where isolated teams work sequentially, creates lengthy back-and-forth cycles that delay time-to-market and prevent the holistic optimization required for next-generation product performance.
This presentation describes a technical approach with an electronics cooling use case to closing this gap through a physics and geometry-aware AI architecture capable of generating CAD-ready 3D geometry directly from high-level design intent. The method integrates spatial reasoning with learned representations of physical behavior, enabling AI-assisted exploration of design spaces that are orders of magnitude larger than those accessible through conventional manual iteration. Unlike black-box optimization tools that remove engineer control, this approach implements a builder-in-the-loop methodology that maintains human oversight while dramatically accelerating the design exploration process.
The architecture combines geometric deep learning models for baseline exploration with localized models for refinement, leveraging non-parametric approaches that can navigate complex, non-linear interactions between design parameters. This dual-model strategy enables efficient exploration of the entire design space while providing targeted optimization within promising regions. For electronics cooling applications, this translates to automated generation and evaluation of thermal management solutions that balance heat dissipation, pressure drop, manufacturing constraints, and geometric packaging requirements simultaneously.
Speaker Bio
Matt Ellis is the Head of Engineering Enablement at Neural Concept, bringing a career spanning technical leadership, product development, and sales in the engineering software and simulation space. He is passionate about pushing the boundaries of product design and helping engineering teams leverage AI to design, simulate, and optimize faster than ever. Matt is a trusted voice in the CAE and engineering intelligence community, regularly engaging at major industry events and working hands-on with customers to turn AI into measurable industrial impact.
From Tools to Agents: How Agentic Engineering Workflows Are Reshaping Simulation-Driven Product Development
Presentation Abstract
Simulation is central to engineering decision-making, yet in many organizations it remains an expert-driven activity rather than a scalable capability embedded across new product introduction (NPI). As product complexity grows and timelines compress, the key challenge shifts from solver accuracy to workflow coordination: when to simulate, at what fidelity, and how results inform decisions.
This talk introduces agentic engineering workflows — AI-driven systems that guide simulation tasks, recommend appropriate model fidelity, and support interpretation while remaining grounded in validated physics. By combining Engineering AI with Physics AI, these workflows move beyond static automation toward context-aware orchestration of design and validation processes.
Examples include AI assistants that assess simulation readiness — reviewing mesh quality, boundary conditions, and convergence behavior — and CAD-triggered workflows that initiate physics-based validation and surface performance trade-offs. Engineers remain in control, with AI operating within defined guardrails.
Agentic workflows enable earlier and broader use of simulation while preserving traceability, verification standards, and domain expertise. Rather than replacing traditional CAE, they represent an evolutionary step in how simulation insight is generated, reused, and governed across the product lifecycle.
Speaker Bio
Continuous Physics Reasoning: Always-On Design Infrastructure
Presentation Abstract
Across every industry that builds physical products, engineering is hitting a structural limit. Products pack more tightly coupled physics at more scales under tighter manufacturing constraints, yet physical reasoning happens only at a few checkpoints: run by a handful of specialists, after the architecture is frozen. The bottleneck is no longer solver capability or compute, but the human effort of turning design intent into analyzable physics and back into decisions.
Continuous Physics Reasoning removes that limit and makes evaluation of the physical behavior accessible across the lifecycle, not at occasional gates. What makes this possible is a foundation model for physics: a single, general-purpose AI model that reasons directly over geometry, materials, and boundary conditions. It generalizes to new designs out-of-the-box, with no per-customer training or per-domain forks, while holding deterministic, solver-grade accuracy at manufacturing resolution.
We ground the principle in its most demanding proving ground: advanced semiconductor component designs, where nanometer features meet centimeter assemblies and correctness is a contractual sign-off requirement. The model returns solver-grade results with sub-percent error against first-principles solvers on geometries unseen in training, including out-of-distribution cases in seconds, not the hours FEM requires. A single model spans steady-state conduction, transient conduction, and thermo-mechanical warpage. We show two decisions this moves upstream: region-aware cooling co-design for 2.5D HBM accelerators, sweeping hundreds of floorplan and cooling combinations to expand thermal margin at fixed cost; and per-die junction-temperature histories under AI mission profiles that drive reliability and throttling-margin design.
The semiconductor industry is where Continuous Physics Reasoning is proven. But the framework, and the foundation-model approach behind it applies across automotive, aerospace, energy, medical devices, and beyond. This talk makes the case that physics belongs at the point of design; always-on infrastructure, not an end-of-cycle gate across every industry building at scale and speed.
Speaker Bio
Rahima K. Mohammed is a Semiconductor Technical Advisor and Distinguished Engineer at Vinci4D, leading architecture and strategy for AI-accelerated, physics-intelligent simulation across component, package, system, and data-center domains. She joined Vinci4D in 2026 after 27 years at Intel, retiring as Senior Principal Engineer. At Intel she architected advanced cooling — vapor chamber, liquid loop, and cold plate — that lifted server SoC thermal margins more than 20%, and led AI/ML adoption across an 850-engineer validation organization with 75+ production use cases and $350M in quality savings.
She did her graduate schooling in Mechanical Engineering from Yale and brings 9 patents and 130+ publications and 20+ keynotes. She was featured into the National Inventors Hall of Fame (2024) and is a recipient of SWE’s Advocate for Women in Engineering Award (2020) and PRISM award (2015). She served as Program Chair of IEEE SEMI-THERM 31 and General Chair of SEMI-THERM 32, and TPC member TestConX workshop.
Measuring Shape Fidelity in Generative CAD Models
Presentation Abstract
Generative AI is rapidly expanding what designers and engineers can create in 3D, but visual plausibility is not the same as geometric fidelity. This presentation asks a practical validation question: how close are AI-generated CAD models really to a target desired shape?
We introduce a feature-vector workflow using the Metafold Shape Similarity technology to compare generated models against reference targets. Each model is encoded into a geometric feature vector, enabling direct comparison through aggregate similarity scores, coordinate-level distance ribbons, scale-normalized metrics, and side-by-side 3D previews. The result is a repeatable method for moving beyond “looks right” evaluation toward measurable shape correspondence.
Using examples from current 3D generative design workflows, the talk demonstrates how feature vectors can expose where a generated model preserves intent, where it drifts, and which geometric features contribute most to the gap.
This approach offers a lightweight validation layer for AI-assisted CAD: fast enough for iteration, interpretable enough for engineering review, and concrete enough to support model benchmarking.
Speaker Bio
Rahima K. Mohammed is a Semiconductor Technical Advisor and Distinguished Engineer at Vinci4D, leading architecture and strategy for AI-accelerated, physics-intelligent simulation across component, package, system, and data-center domains. She joined Vinci4D in 2026 after 27 years at Intel, retiring as Senior Principal Engineer. At Intel she architected advanced cooling — vapor chamber, liquid loop, and cold plate — that lifted server SoC thermal margins more than 20%, and led AI/ML adoption across an 850-engineer validation organization with 75+ production use cases and $350M in quality savings.
She did her graduate schooling in Mechanical Engineering from Yale and brings 9 patents and 130+ publications and 20+ keynotes. She was featured into the National Inventors Hall of Fame (2024) and is a recipient of SWE’s Advocate for Women in Engineering Award (2020) and PRISM award (2015). She served as Program Chair of IEEE SEMI-THERM 31 and General Chair of SEMI-THERM 32, and TPC member TestConX workshop.
Before the Model: Aurora as AI-Grounded Climate Intelligence for Early-Stage Design
Presentation Abstract
The environmental design conversation typically begins too late, after massing is committed and geometry is locked. Aurora repositions that conversation to day one, giving architects and engineers an immediate, evidence-based picture of any site before a design tool is opened.
The platform synthesises EPW weather files, CMIP6 climate projections, ASHRAE design conditions, NOAA historical records for US sites, and seismic hazard data into a unified analysis environment, covering temperature and humidity distributions, wind roses, solar radiation by facade orientation, thermal comfort (UTCI, PET, SET), rainfall, carbon intensity, and future projections to 2050. Every analysis is immediately exportable as a formatted PDF or PowerPoint report, ready to present to a client or design team without additional preparation.
Two integrated AI layers surface this data as design insight. A streaming conversational assistant powered by Google Gemini is grounded in the actual EPW and CMIP6 data for the active location. It explains what site climate means for design decisions, renders specific charts inside the conversation, and adjusts application settings through natural language. A second layer generates AI-written summary cards for ASHRAE design conditions and 2050 climate outlooks, automatically scoped to the site.
Aurora does not generate or evaluate geometry. This is intentional: it is a pre-design climate intelligence layer, not a replacement for tools like Autodesk Forma. It provides the environmental literacy that should inform every decision made in those tools, a shared language between architect, engineer, and client before a single wall is drawn.
Speaker Bio
Damola Michael is an Associate Computational Design Specialist at CannonDesign, where he leads the development of internal computational design focused tools that bridge architectural practice and software engineering. A Chartered Architect registered with the ARB, RIBA, and the State of Washington, and a member of the American Institute of Architects, he holds advanced degrees in Architecture and Computer Science from the Universities of Portsmouth and York.
Damola specialises in building computational systems that make complex design intelligence accessible at the earliest stages of a project. His work includes Aurora, an AI-grounded climate analysis platform used across CannonDesign, and CannonFly, a Grasshopper plugin suite for multi-disciplinary design workflows. His research has been published in collaboration with Speckle.
Physics as Infrastructure for the AI Era
Presentation Abstract
AI is reshaping engineering as it drives demand for surrogate models, large design studies, and agentic workflows that require automated simulation loops at scale. These pipelines collide with two realities: geometric representation is fragmented throughout the hardware lifecycle (from CAD to point clouds), and traditional FEA, reliant on conformal meshing and manual preprocessing, treats human intervention as a core requirement. This brittle paradigm resists automation.
Scaling simulation for the AI era is fundamentally an infrastructure problem. Using immersed grid methods, Intact natively ingests any geometry representation without preprocessing, exposing physics as an API-first callable function. We demonstrate how this architecture powers the emerging “engineering-as-code” stack through automated DOE pipelines and LLM orchestration via MCP across concept, manufacturing, and deployment.
Speaker Bio
When Failure Is Not an Option: Bringing Certifiable AI to Engineering Design
Presentation Abstract
Artificial intelligence is poised to automate a large share of design engineering work, yet the technology that excites the commercial world poses a fundamental problem for high-consequence industries. Generative AI is probabilistic by nature. It produces plausible answers, not provably correct ones. In sectors where a single structural failure can ground a fleet, halt a production line, or cost lives, plausibility is not enough. The question is no longer whether AI will transform engineering, but whether we can trust it when failure is not an option.
This talk presents a different path. Cognitive Design Systems is a design exploration platform for mechanical and thermo-mechanical component design. Rather than embedding opaque AI inside traditional CAD software, we bring proven engineering workflows to the AI. Deterministic solvers for topology optimization, finite element analysis, manufacturing-driven design, and cost and carbon assessment produce repeatable, auditable, physically grounded results. A conversational AI layer orchestrates these solvers, interpreting intent and chaining tasks, while the underlying engineering computation remains fully deterministic and traceable. Engineers gain dramatic speed without surrendering verifiability or control.
This is not theoretical. Our approach is shaped by work with demanding industrial leaders including Safran, Thales, MBDA, Toyota, Tetra Pak, and Logitech, spanning aerospace, automotive, defense, and industrial machinery. These are organizations where engineering rigor and certification are non-negotiable.
The implications reach across every engineering sector. As manufacturers face mounting pressure to lightweight structures, accelerate certification, reduce cost and carbon, and modernize their industrial base, the ability to design qualified components faster, with full auditability, becomes a decisive advantage. Trustworthy AI is not a constraint on innovation. It is the precondition for deploying AI in the systems the world depends on. Attendees from industry and policy alike will leave with a clearer view of what responsible, deployable AI for high-consequence engineering actually looks like.
Speaker Bio
Organization:
Pasteur Labs
Presenter:
Alexander Lavin
Pasteur Labs
Presentation Abstract
Speaker Bio
Alexander Lavin is a leading expert in AI-for-science and probabilistic computing. He’s Founder & CEO of Pasteur Labs (and non-profit “sister” Institute for Simulation Intelligence), reshaping R&D with a new class of AI-native simulators, commercializing in energy security, aerospace, materials & manufacturing sectors.
For the last dozen years, Lavin has focused on artificial general intelligence (AGI) research with top startups in neuroscience and robotics (Vicarious, Numenta), and sold his prior ML-simulation startup Latent Sciences to undisclosed pharmaco in neurodegeneration R&D. Lavin also serves as AI Advisor for NASA, overseeing physics-ML efforts for the NASA-ESA “Digital Twin Earth” projects. Previously, Lavin was a spacecraft engineer with NASA and Blue Origin, and won several international awards for work in rocket science and space robotics (including Google Lunar XPrize during graduate studies at Carnegie Mellon). Lavin was named Forbes 30 Under 30 in Science, and a Patrick J. McGovern Tech for Humanity Changemaker
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