David Burpee · MorphXgen

Many Additive footwear platforms promise a level of customization beyond traditional footwear manufacturing processes, but how custom is it really? This presentation will explore a variety of strategies for the integration of various custom data into bespoke additive-ready algorithms in footwear design. The ability to create force-responsive structures through various types of micro lattice to tune flexibility, gradient behavior, anisotropy all within the confines of additive manufacturability parameters. Approaches include multi-agent algorithms with intelligent emergent behavior, simulation-driven anisotropic stochastic lattice structures, and body-responsive data mapping for truly one-of-one additive footwear creations.

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Sarat Babu · SARAT

Computational design approaches to optimization are often constrained by the tools available and the established methods for generating optimal forms. With AI coding tools, the barrier for designers to develop their own software and pipelines has dropped considerably.

Sub-Optimal reframes optimization around two priorities: preserving the original designer’s macro intent, and using geometry to address the anisotropic surface roughness of additive manufacturing. The result is neither lattice application nor topology optimization, but a grown internal structure driven by simulation, surface texture and build orientation that remains largely invisible at the macro level.

The talk covers the trust gap senior engineers face with optimized DfAM parts, the use of surface texture to mask layering defects and build orientation, and how the pipeline was built in six weeks for about $400 in AI tokens using open-source libraries including geometry3Sharp and OpenVDB, with 3MF as the output format.

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This episode is the opening keynote from CDFAM Tokyo 2026, presented by Guido Cossu and Ivo Timóteo of Braid.

Braid was founded in Tokyo in 2020 by a team drawn from physics, applied mathematics, computational geometry, large-scale simulation and machine learning. At the time, the prevailing view in AI held that scaling end-to-end learning on ever-larger datasets would solve most problems. Braid took a different position: engineering design, where physical accuracy, safety and manufacturability are non-negotiable, requires reasoning from the laws of physics and the constraints of manufacturing rather than pattern-matching from prior examples.

Their system, AURA, combines learning with formal reasoning to move from engineering intent to manufacturable geometry, replacing the manual draw, simulate, review and redraw loop with structured exploration of alternatives that the engineer evaluates and selects from.

In this talk, Cossu and Timóteo explain the semantic architecture behind that approach and show results from deployments in Japanese manufacturing, across several mass-production processes with little in common.

The Best Design Is The Design You Can Make

Guido Cossu; Ivo Timóteo · Braid

Across computational design, the part that performs and the part that can be built are rarely the same part: optimization and generative methods meet the functional targets, and the work of making the result manufacturable happens afterward, by hand, often negating the performance that was gained. Braid closes that gap differently. Rather than optimizing a seed geometry or generating candidates from data, the system designs de novo — geometry reasoned directly from what an engineer specifies: function, physical conditions, the manufacturing process the part must suit, the cost it must meet.

The mechanism is semantic reasoning rather than data-driven generation: a semantic architecture encodes physics, geometry, and manufacturing constraints with their relationships, and enforces constraint satisfaction by construction. Because the manufacturing process is reasoned from rather than filtered for, the geometry is manufacturable from the first output, not repaired into manufacturability afterward.

Braid demonstrate this on real engineering components across several conventional mass-manufacturing processes, each with its own design constraints. The examples come from deployments in Japanese manufacturing industries where tolerances are tight and errors are costly. One system handling processes with so little in common is the crucial point: each result is reasoned from principle, not retrieved from examples of similar parts — which is, in the end, the difference between a design system you can trust on a novel part and one you can only trust on a familiar one.

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Giorgia Cannici · Virginia Tech

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.

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Matthew Shomper · Not a Robot Engineering

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.

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Damola Michael; Elliot Glassman · CannonDesign

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.

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Matthew Sederberg · Coreform

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.

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Mike Park · Flexcompute

Computational design enables wider configuration exploration and reduces time to market, but turning innovation into validated, manufacturable designs is hampered by human-in-the-loop simulation. Manual geometry cleanup, meshing, and data management pace every workflow, while the feedback loops between simulation and the rest of the design organization stay broken by data silos. This talk presents an end-to-end, AI-native approach to closing those loops. GeometryAI applies a novel, topology-aware geometry representation that takes raw CAD to analysis-ready models automatically with mathematical guarantees that facilitate meshing and analysis. Physics-agnostic output feeds CFD, structural, and thermal tools with direct connections to surrogate training and inference. The GPU-native solver Flow360 delivers high-fidelity physics at speeds that tighten feedback loops. Thread ties it together with automatic data lineage, so every run is reproducible, every result is traceable, and institutional knowledge becomes the starting point rather than the bottleneck. Standard web interfaces and Python APIs enable humans and their agents equally. Real-world results are validated with industry-standard AIAA High-Lift Prediction Workshop submissions. Full-aircraft hover analysis is possible in hours. We show how geometry-aware automation, GPU-native simulation, and complete traceability make simulation-driven design genuinely agent-ready, at the scale and speed modern programs demand.

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Andreas Vlahinos · Advanced Engineering Solutions

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.

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Steve Massey · SysGit

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.

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Methods are presented, compared, and questioned, but the record of that work often ends with the recording. We see value in more of it being tested, documented, and published where others can build on it.

npj Engineering Intelligence is a new, open access Nature Portfolio journal, now open for submissions.

Led by founding Editor-in-Chief Chris McComb, it publishes research at the intersection of AI and engineering: work that advances how engineers discover, design, build, operate, and sustain physical systems, as well as engineering challenges that inspire better AI methods.

For the CDFAM community, Chris is especially interested in research on computational design, additive manufacturing, and the path from requirements to manufactured parts.

A compelling paper should show more than an impressive demonstration. What did the method enable engineers to learn or do, how does it compare with credible alternatives, and where does it fail under real engineering constraints?

Researchers and industry teams with findings others can test and build on are encouraged to explore the journal’s scope and submit their work.

Learn More

If you need to cite anything mentioned on stage at CDFAM, we have made it easier to do so with the CDFAM Index tool that has the ability to search the transcript to specific words, speakers and events, find the exact timecode in Youtube and add the academic citation of your preferred flavour.

We are looking forward to seeing you CDFAM presentations expanded, peer reviewed and published with npj Engineering Intelligence.

Michael Frei · ARENA Physica

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.

This talk shares our vision for the future of AI-driven electromagnetic design.

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Sai Nelaturi · C-Infinity

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.

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Juan Alonso · Luminary

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.

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Join leading experts in computational design at all scales for two days of knowledge sharing and networking at CDFAM in Tokyo, October 8-9, 2026

Alexander Htet Kyaw · MIT

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.

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Brandon DeMille · General Atomics Aeronautical Systems

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.

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Many generative design tools produce geometry and leave manufacturability to be resolved afterward.

Braid Technologies, a Tokyo-based team of around thirty researchers and engineers, has taken the opposite position with AURA, an autonomous engineering system that starts from requirements rather than shape and treats the production process as an input from the first step.

Ahead of their keynote at CDFAM Tokyo, Guido Cossu explains what the company is building, why they began with stamping and casting in automotive and aerospace, and what they mean when they say a machine has actually engineered a part.

Starting from requirements, not geometry

The founding premise is that engineering will not be automated by scaling data alone. Physical design requires learning, physics, geometry and manufacturing knowledge to be solved together, and the result has to be explainable to the engineer who signs off on it.

The Tokyo presentation is built around a single question: when can we say a machine has engineered a part? Braid’s answer is that design must begin with requirements, physics and manufacturing must be resolved simultaneously, and the output must carry a rationale any engineer can follow.

Where the engineer sits

AURA does not remove the engineer. It moves them to defining intent and deciding which tradeoffs matter. Changing a stiffness target, a mass limit or the manufacturing process produces a new design for the revised problem, and each result exposes something about the wider design space.

Why stamping first

Braid deliberately began in high-volume automotive and aerospace, where small compromises in mass or development time compound across many parts and many vehicles, and where a shape the factory cannot make has no value. Stamping is the first process supported in the software product. AURA has also been applied to injection molding and die casting.

The workflow is currently file-based rather than integrated with PLM or PDM. Engineers input standard CAD geometry, material, functional requirements and manufacturing method. AURA returns manufacturable designs in standard CAD formats with an explanation of performance and rationale, which teams verify with their own solvers and approve through existing sign-off processes.

Read Full Interview

CDFAM Tokyo, October 8-9, 2026

Braid Technologies is one of 32 speakers presenting at CDFAM Tokyo at the Tokyo International Forum, covering computational design, AI and machine learning for design, engineering and architecture at all scales.

TOKYO CDFAM PROGRAM

Chris Helmerich · Celedon Solutions

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.

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Jan Vandenbrande · nTop

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.

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William Steadman · Quanscient

We present the results of the largest CFD simulations to date deployed on IBM and IonQ quantum hardware through our ongoing collaborations across the aerospace, maritime, and automotive sectors. We will explore the critical trade-offs quantum computing introduces to computational design in aerodynamics and aeroacoustics, while demonstrating how these advancements are being integrated into Quanscient’s multiphysics software.

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