CDFAM Computational Design Symposium

Duann Scott
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Recordings of presentations from the CDFAM Computational Design Symposium held worldwide. Leading experts in computational design, AI and machine learning for industrial design, engineering and architecture from industry, academia and software development. www.designforam.com

  1. 2d ago

    When Failure Is Not an Option: Bringing Certifiable AI to Engineering Design

    CDFAM Computational Design Symposium — Washington DC 2026 Rhushik Matroja · Cognitive Design Systems 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. Links Talk page with full transcript Watch the talk on YouTube Cite this talk Search this talk's transcript This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.designforam.com

  2. 2d ago

    Measuring Shape Fidelity in Generative CAD Models

    CDFAM Computational Design Symposium — Washington DC 2026 Daniel Hambleton · Metafold 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. Links Talk page with full transcript Watch the talk on YouTube Cite this talk Search the full text of every recorded CDFAM presentation This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.designforam.com

  3. 2d ago

    Requirements to Production Part in Minutes: How Physical AI Closes the Loop Between Optimization and Manufacturing

    CDFAM Computational Design Symposium — Washington DC 2026 TJ Root · InfinitForm 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. Links Talk page with full transcript Watch the talk on YouTube Cite this talk Search the full text of every recorded CDFAM presentation This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.designforam.com

  4. 3d ago

    Agentic Engineering: Generative AI in structural applications

    CDFAM Computational Design Symposium — Washington DC 2026 Sergey Pigach · CORE studio | Thornton Tomasetti CORE studio spent a decade building machine learning tools for structural design and analysis, all running as cloud services behind APIs. When MCP arrived, handing those same tools to an agent turned out to be close to trivial. Sergey Pigach demonstrates Bender, an agentic system running on AWS that the firm talks to through Slack: ask it for a concrete column stack and footing for a five-storey residential building in New York, let it make the remaining assumptions, and it calls the tools the engineers use, renders the result and writes a design summary. It lives in Slack deliberately, because an agent sitting in a shared thread already has the context of the conversation around it, which a one-to-one chatbot does not. Specialist sub-agents handle questions like embodied carbon. From there the talk moves to agents talking to each other. A2A is a protocol for delegation between agents, complementary to MCP rather than competing with it, but it has no discovery layer — a public agent the team put online was found by nobody. That gap prompted Waggle, Pigach's own side project, which crawls for valid agent cards and builds a searchable index with health, quality and trust signals, then delegates a request to whichever agent can handle it. He also shows agents paying each other small amounts to cover expensive work. The most uncomfortable result is a benchmark. CORE studio asked its own engineers for the hardest structural problems they could devise, assembled 91 of them, graded answers to within one percent, and gave the models nothing but a calculator and a Python sandbox — no internet, no engineering software. They expected around half. It saturated immediately, with the leading models above 94 percent. Tracing backwards showed the unlock was reasoning: the first reasoning model jumped from 38 percent to 74, and the line has run straight up since. Structural engineering, as he puts it, is a verifiable domain. The talk closes on CAD experiments, including a Grasshopper plugin that exposes a parametric definition to an agent as MCP tools and a hackathon robot arm driven by natural language, and on the conclusion that this is not a domain expertise problem but an unhobbling problem. Links Talk page with full transcript Watch the talk on YouTube Cite this talk Search the full text of every recorded CDFAM presentation This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.designforam.com

  5. 3d ago

    Physics as Infrastructure for the AI Era

    CDFAM Computational Design Symposium — Washington DC 2026 Rik Baruah · Intact Solutions 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. Links Talk page with full transcript Watch the talk on YouTube Cite this talk Search the full text of every recorded CDFAM presentation This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.designforam.com

  6. 3d ago

    From Tools to Agents: How Agentic Engineering Workflows Are Reshaping Simulation-Driven Product Development

    CDFAM Computational Design Symposium — Washington DC 2026 Andrew Acuff · SimScale 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. Links Talk page with full transcript Watch the talk on YouTube Cite this talk Search the full text of every recorded CDFAM presentation This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.designforam.com

  7. 4d ago

    Text To Spaceship: Accelerating Mission Development With AI at NASA

    CDFAM Computational Design Symposium — Washington DC 2026 Ryan McClelland · NASA Goddard 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. Links Talk page with full transcript Watch the talk on YouTube Cite this talk Search the full text of every recorded CDFAM presentation This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.designforam.com

  8. 6d ago

    Authoring Autonomy

    CDFAM Computational Design Symposium — Washington DC 2026 Brian Ringley · Boston Dynamics Atlas is a general-purpose humanoid aimed squarely at industrial work, and Brian Ringley makes the case that its value is economic rather than technological. Most of the automation gaps on a factory floor could be closed with conventional equipment; what makes that impractical is designing a bespoke solution for each one. A single investment in generalised hardware turns all of those separate problems into one software problem, which is a far cheaper thing to solve. The talk walks through the industrial design decisions that follow from putting a machine into shared human space. Why the robot has a head and a face that turns: perception needs to sit at eye level, and a gaze tells a person nearby that they have been seen and hints at what the robot will do next. Why it reads as equipment rather than as a person. And why only two actuator types appear across the whole machine, giving a blocky, repetitive design language in exchange for cost, reliability and field-replaceable parts, with continuously rotating joints that let it work in ways a human body cannot. The second half is about teaching it to do useful work. Ringley lays out the current stack — reinforcement learning for whole-body control, behaviour cloning from VR teleoperation for manipulation, and a vision-language model above both for reasoning and tool calls — and is candid that the hard constraint is data. There is no internet-scale corpus of action data, so it has to be produced: pilots suited up in VR on real plant floors, training in simulation to remove latency, and supervised correction where a human takes control mid-policy to annotate what went wrong. Running underneath is an argument about authorship, that the line between who writes software and who uses it has largely dissolved, and that the world itself is becoming the arena in which this software is trained. Links Talk page with full transcript Watch the talk on YouTube Cite this talk Search the full text of every recorded CDFAM presentation This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.designforam.com

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Recordings of presentations from the CDFAM Computational Design Symposium held worldwide. Leading experts in computational design, AI and machine learning for industrial design, engineering and architecture from industry, academia and software development. www.designforam.com

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