CDFAM Computational Design Symposium

Recorded at CDFAM Computational Design Symposium, NYC, October 29-30, 2025https://cdfam.com/nyc-2025/nTopBradley RothenbergFrom 3 Configurations to 300: Rapid Trades for Advanced Aircraft DesignPresentation AbstractAircraft development timelines have collapsed from 7 years to 18 months, but design tools still assume you have years to iterate. The result: teams freeze architecture in week 2, before they understand the design space, and spend the rest of the program managing the consequences.The core problem is going from requirements-to-design is just too slow. Serial design evaluations, manual CAD updates that fail to parametrize correctly, and expensive simulation cycles create weeks-long iteration loops.nTop solves this through three architectural principles: Parametric models that remain robust under any design change. No geometry failures, no manual repairs; integrated notebooks capturing engineering knowledge in executable form; and GPU-native solvers enabling interactive design-analysis cycles with performance feedback in minutes.What’s the alternative? Exploring 3-4 hand-crafted configurations slowly or quickly committing to a single concept. Neither is likely to win. nTop enables systematic exploration of hundreds of variants in the time that traditional approaches evaluate three.This presentation demonstrates real examples: Group 1-3 UAS configurations generated and flight-tested in weeks, hypersonic vehicle trade studies evaluating hundreds of variants, and rapid weapons platform sizing with integrated CFD.The result: teams explore more, fail fast, and learn faster—improving win rates through comprehensive trade studies and defensible performance predictions.About CDFAM:CDFAM is a global symposium series at the forefront of computational design, advanced manufacturing, and performance-driven engineering. With a strong emphasis on innovation, CDFAM highlights how leading companies and researchers are leveraging AI, machine learning, and simulation technologies to drive the next generation of design tools, workflows, and digital fabrication methods.The symposium fosters cross-disciplinary collaboration and knowledge exchange between designers, engineers, and technologists exploring the cutting edge of digital design — from generative workflows.Past presenters and partners include companies such as NVIDIA, NASA, New Balance, BMW, ARUP, Foster +, Partners, BIG, Autodesk, Dassault Systèmes, nTopology, PyhicsX, Neural Concept, Siemens and more, showcasing how computation and AI are transforming everything from aerospace to footwear.Learn more at https://cdfam.com 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

Recorded at CDFAM Computational Design Symposium, NYC, October 29-30, 2025https://cdfam.com/nyc-2025/Organization:Pasteur LabsPresenter:Alexander LavinThe Unreasonable Effectiveness of Simulation IntelligencePresentation AbstractScientific rigor & engineering reliability have always been important yet contentious topics in the AI field. Recent AI trends crank up model sizes, but at what costs? Transparency and verifiability, amongst others that are core to industrial R&D—not to mention the massive spending. These costs are perhaps felt the most in physics simulation and digital engineering. Enter simulation intelligence (SI). SI is not antithetical to AI, rather it is the pragmatic approach to bringing AI capabilities into industrial R&D. Rather than LLMs atop legacy engineering tools or Foundation Models to opaquely replace physics solvers, we look to the combinatorial possibilities available when SI motifs are brought together—namely differentiable physics programming and surrogate modeling, yielding multiphysics modules. This talk will describe the distinction, that is: static CAE simulations vs dynamic simulators, bespoke surrogate models vs flexible multiphysics modules, massive black-box AI vs efficient programmatic SI. Examples from the SI Platform will elucidate end-to-end digital engineering pipelines, in diverse sectors from nuclear energy and data centers, to aerospace and automotive safety.Speaker BioAlexander 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 (https://simulation.science). 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 ChangemakerAbout CDFAM:CDFAM is a global symposium series at the forefront of computational design, advanced manufacturing, and performance-driven engineering. With a strong emphasis on innovation, CDFAM highlights how leading companies and researchers are leveraging AI, machine learning, and simulation technologies to drive the next generation of design tools, workflows, and digital fabrication methods.The symposium fosters cross-disciplinary collaboration and knowledge exchange between designers, engineers, and technologists exploring the cutting edge of digital design — from generative workflows.Past presenters and partners include companies such as NVIDIA, NASA, New Balance, BMW, ARUP, Foster +, Partners, BIG, Autodesk, Dassault Systèmes, nTopology, PyhicsX, Neural Concept, Siemens and more, showcasing how computation and AI are transforming everything from aerospace to footwear.Learn more at https://cdfam.com. 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

Recorded at CDFAM Computational Design Symposium, NYC, October 29-30, 2025https://cdfam.com/nyc-2025/Organization:ARENA-AIPresenter:Pratap RanadeArtificial Intuition: Building an AI Mind for Electromagnetic DesignPresentation AbstractMost 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. In this talk, I’ll share how we’re pushing past that frontier by creating artificial intuition — AI systems that learn physical behavior inductively, not deductively. Drawing inspiration from quantum experiments like the Kondo mirage, where discovery outpaced simulation, I’ll show how our team built Atlas: an AI that learns directly from electromagnetic test data to verify, optimize, and eventually postulate new designs. We’ll share results from realworld applications in semiconductors and aerospace, and offer a teaser of what’s to come over the next twelve months.Speaker BioPratap Ranade, CEO and Founder of ARENA-AIJoin us at CDFAM Barcelona April 8-9, 2026, the premier symposium for computational design, AI, and engineering innovation.Don’t miss your chance to connect with global leaders in design and technology.Register by February 1st to secure the early bird rate and be part of the conversation shaping the future of design, architecture, and advanced manufacturing. 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

Recorded at CDFAM Computational Design Symposium, NYC, October 29-30, 2025https://cdfam.com/nyc-2025/Organization:University of Southern CaliforniaPresenter:David GerberDigital Twining a Living LabPresentation AbstractThe Living Lab Project is an innovative Viterbi initiative designed to enhance academic research and provide practical learning experiences through real-time monitoring and analysis of the new Ginsburg Hall building. Leveraging sensors embedded in the building’s systems and integrating cutting-edge digital twin technology, this project captures and analyzes data on energy usage, water consumption, building health and occupant well being, and more, offering a comprehensive dataset for faculty and student research. The project treats our newest building, a LEED platinum accredited building, as a scientific instrument to support both near term and longitudinal research across a multitude of disciplines including but not limited to Human Building Interaction, to AI, and sustainability related research fieldsSpeaker BioDr. Gerber holds a joint appointment at USC’s Viterbi School of Engineering and the USC School of Architecture as a Professor of Civil and Environmental Engineering Practice and of Architecture. Dr. Gerber is the program Director for the Civil Engineering Building Science undergraduate program and the program Director for the Masters of Science in Emerging Technologies for Construction Program. Dr. Gerber is an associate director in the Office of Technology Innovation and Entrepreneurship. He teaches in the Viterbi School of Engineering, the School of Architecture and at the Viterbi Startup Garage. Dr. Gerber’s professional experience includes working in architectural, engineering and technology practices in the United States, Europe, India and Asia for Zaha Hadid Architects in London; for Gehry Technologies in Los Angeles; for Moshe Safdie Architects in Massachusetts; The Steinberg Group Architects in California; and for Arup as the Global Research Manager. Dr. Gerber’s research has been industry, fellowship, DoD, and NSF funded and is focussed on the development of innovative systems, tools, methods for design of the built environment. He has developed digital twin technologies and advises, and co advises PhD students from Architecture and Engineering on topics that integrate computer science, robotics, engineering, with architecture. David Gerber received his undergraduate architectural education at the University of California Berkeley (Bachelor of Arts in Architecture, 1996). He completed his first professional degree at the Design Research Laboratory of the Architectural Association in London (Master of Architecture, 2000), his post professional research degree (Master of Design Studies, 2003) and his Doctoral studies (Doctor of Design, June 2007) at the Harvard University Graduate School of Design. Dr. Gerber was the recipient of the Frederick Sheldon Fellowship at Harvard University and was a Research Fellow at MIT’s Media Lab in the Smart Cities group. 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

Recorded at CDFAM Computational Design Symposium, NYC, October 29-30, 2025https://cdfam.com/nyc-2025/Organization:CORE studio | Thornton TomasettiPresenter:Sergey PigachEngineering Intelligence: Practical Applications of AI in Structural Engineering PracticePresentation AbstractArtificial Intelligence is no longer a distant future. It is actively shaping how structural engineers work, collaborate, and innovate. This session offers a practical look into how Thornton Tomasetti’s CORE studio is advancing AI integration within the firm, with a focus on real-world tools and workflows that enhance engineering practice. Attendees will explore the firm’s hands-on experimentation with generative models, domain-specific co-pilots, and applications of agentic workflows, as well as strategies for cross-disciplinary collaboration that ensure AI tools align with engineering priorities. The presentation will also share lessons learned in promoting firmwide adoption, cultivating technical fluency, and building an inclusive innovation culture that empowers all team members to contribute to AI-driven transformation.Speaker BioSergey 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. 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

Recorded at CDFAM Computational Design Symposium, NYC, October 29-30, 2025https://cdfam.com/nyc-2025/Organization:NVIDIAPresenter:Ian PeglerKeynote Presentation: How NVIDIA Is Accelerating Product DevelopmentPresentation AbstractComputational simulation and design have transformed product development by significantly reducing time and costs. However, designing complex products remains a challenging and resource-intensive process.In this presentation, we will explore key industry challenges and demonstrate how NVIDIA is leveraging innovative solutions to address them. Specifically, we will highlight the use of accelerated computing to enable faster, higher-fidelity simulations, and AI surrogate models to provide designers with real-time feedback.Additionally, we will discuss integrated approaches that combine these technologies to create responsive, real-time digital twins. The foundational platform supporting these advancements will be examined, along with real-world industry applications illustrating their impact.Speaker BioIan Pegler is a member of the Computer-Aided Engineering (CAE) team at NVIDIA. With a career largely focused on computational fluid dynamics (CFD), Ian has extensive experience across various industries, including aerospace, automotive, energy, and marine. Currently, he collaborates with small and start-up CAE companies to help accelerate their engineering tools and workflows. Ian holds a Master’s degree in Aerospace Engineering from the University of Southampton, UK, and is based in Chicago.Join us at CDFAM Barcelona, April 8-9, 2026 to connect with leading designers, engineers and architects at the forefront of the adoption of AI and Machine learning through computational design for two days of knowledge sharing and networking. 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

Recorded at CDFAM Computational Design Symposium, NYC, October 29-30, 2025https://cdfam.com/nyc-2025/OrganizationDavid Burpee DesignPresenter:David BurpeeComputational Morphogenesis: Leveraging Proceduralism to Unlock Temporal DesignPresentation AbstractCurrent paradigms of design and engineering operate on the premise that realized designs are static – that is once they are designed and manufactured they exist in their final state. Likewise even flexible computational systems tend to not incorporate the dimension of time as a design tool. Despite dozens or hundreds of sliders, variables, and graphs, most products – even those designed computationally – are “frozen” at a certain point and designed as a static object.New advancements in material science research particularly around Engineered Living Materials or ELMs have elucidated these shortcomings in our design and engineering workflows. How can we model, simulated, validate, product performance or behavior in this dynamic, temporal environment? We need new processes, workflows, methods, and tools in order to effectively utilize this new dimension of material typologies, as well as continue to design in ways that are more connected to engineering simulation and validation.In this presentation I will explore the use of proceduralism as an essential creation environment that is uniquely able to design in conjunction with these temporal constructs. I will present a subset of my work that utilizes computationally-driven simulations for the creation of physical product, as well as some of my teaching and research through the NSF grant project designing with Engineered Living Materials at the University of Washington.Speaker BioDavid Burpee is a multidisciplinary Computational Design Leader based in the Pacific Northwest, with expertise spanning Footwear, Apparel, Consumer Goods, Automotive, Medical, and Architecture industries. He lectures on Computational Design and Algorithmic Thinking at the University of Washington and is a Computational Researcher on a National Science Foundation grant exploring Engineered Living Materials (ELMs).With over a decade of Computational Design experience, David has delivered advanced design strategy, tools, and training for companies including Nike, PUMA, FILA, General Motors, Harry’s Razors, and EQLZ. His work demonstrates a proven methodology that merges creativity, deep technical capabilities, and broad market impact.Originally trained as an Architectural Designer with a Master of Architecture from USC, David has contributed to highrise and supertall projects in Los Angeles, Seattle, and across Asia. His work integrates computational approaches at every scale, from skyscrapers to small installations.Driven by a passion for biomimicry, generative systems, and sustainable innovation, David applies computational design to address complex ecological and social challenges through creative, high-performance solutions.Join us at CDFAM Barcelona, where the forefront of computational design and advanced manufacturing comes alive. This gathering brings together innovators, researchers, and industry leaders to explore the future of design through simulation, generative tools, and performance-driven workflows. Set in one of Europe’s most dynamic creative hubs, CDFAM Barcelona is the place to connect, learn, and be inspired by what's next in the world of computational fabrication. 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

Recorded at CDFAM Computational Design Symposium, NYC, October 29-30, 2025https://cdfam.com/nyc-2025/Organization:New BalancePresenter:Samuel WhitworthComputational Craft: One Footwear Designer’s Quest To Replace HimselfPresentation AbstractFootwear design, like many design domains, has long been defined by the combination of two-dimensional drawings and designers’ intuition. While these remain important elements of the field, various digital design methods are currently surging and have significantly altered the traditional footwear design process. This presentation will explore the opportunities presented by this shift through the lens of my own experience as an industrial designer turned computational designer—specifically how the application of computational methods has allowed me to expand the types of design solutions I can explore. In this sense, it’s been a journey of “replacing” my traditional industrial design role with a new hybrid role defined by what I call “computational craft.”Computational craft can be defined as a collaborative human/computer design approach, where the computer extends the reach of the human designer, while the human grounds computational results in the real world of manufactured objects and human sensibility. I will demonstrate several examples of this method in Grasshopper, including Kangaroo-based simulations, multi-objective optimization, and mesh generation/manipulation. Audience members will be able to take away new inspiration for using computational methods in their design workflows, and a feeling of confidence that computational design is accessible to anyone regardless of academic or professional background.Speaker BioSamuel Whitworth is a Computational Designer II at New Balance Athletics, where he has contributed to both inline and innovation projects for the past six years (recent releases include the SuperComp Elite v4 and More v5.) Sam focuses on the intersection of footwear geometry and function using scripting, simulation and functional prototyping, leveraging deep skillsets in both Grasshopper and Blender. He holds a Bachelor of Fine Arts in Industrial Design from Brigham Young University. 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

OrganizationVARIANT3DPresenter:Will SamosirKnit Everything: Surfaces, Systems, and the Future of TextilesPresentation AbstractWhat if anyone who can draw could knit?VARIANT3D exists to break down the barriers to textile manufacturing. Our proprietary software LOOP is the first and only WYSIWYG CAD system for knitting that requires zero knowledge about how knitting works.Unlike conventional knit engineering, which demands months of expert iteration, LOOP lets anyone access a vast library of knit structures and generate machine-ready files in minutes, bringing industrial complexity down to a creative interface. From instant prototyping to scalable product lines, our platform also supports automated calibration and grading. In a world saturated with cut-and-sew fabrics, we’re pioneering a decentralized, on-demand, and zero-waste model of textile production.Beyond that, we recognize that knitting is a medium that blends the language of computation, powerfully soft and flexible materials, as well as pure, collaborative human ingenuity. At VARIANT3D, we’re not just building tools—we’re also cultivating a new language for textile and material innovation. We are excited to share how this vision has shaped our process and journey as an organization, and how we are empowering the future of textiles.Speaker BioWill Samosir is the CTO and Co-Founder of VARIANT3D, where he champions a future that is expressive, adaptive, sustainable, and open. He leads a multidisciplinary team and spearheaded the development of LOOP, a state-of-the-art software platform that reimagines how textiles are made—and who gets to make them. His life’s work is rooted in the belief that humans and computers are co-authors, and that our relationship with complex systems should be intuitive and human-centered.Will is also obsessed with computational geometry, topology, generative design, and emergent behavior. His favorite language is Python, and he’s drawn to all things polymorphic—surfaces, materials, tactile stuff, naming systems, myth and mythology, the many languages of art, and how tools shape thought. He loves music and live shows, and if you’re lucky, you might catch him biking through the summer streets of Brooklyn! 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

OrganizationEaton + Intact SolutionsPresenter:Karthik Rajan VenkatesanNeel KumarAccelerating Metal-to-Plastic Conversion with AI, Implicit CAD, and Mesh-Free SimulationPresentation AbstractThis work presents a simulation-driven generative design framework for reengineering a metallic explosion-proof enclosure into a lightweight, injection-molded fiber-reinforced plastic alternative. The methodology integrates advanced process and performance simulations with AI-guided optimization to enable rapid, intelligent design iteration.Central to this workflow is the use of implicit CAD modeling in nTop, which allows for highly flexible and parameterized geometry generation, seamlessly integrated with a robust, mesh-free simulation engine from Intact Solutions. This combination eliminates traditional meshing bottlenecks and enables direct evaluation of complex geometries without meshing or format conversion.The workflow is executed in two stages. Stage I establishes baseline using Moldflow for plastic flow simulation, Digimat for fiber orientation mapping, and ABAQUS for traditional FEA, culminating in a stress field point cloud. Stage II transitions to an AI-driven design space exploration loop, where models are trained and evaluated through a Bayesian optimization framework. The implicit CAD models are directly analyzed using Intact.Simulation for Automation without any manual pre-processing, enabling a seamless feedback loop between design and performance while supporting rapid, large-scale design iterations.This approach exemplifies the power of computational design at scale—reducing turnaround time from over 48 hours with traditional CAD and FEA methods to under 1.5 hours with the full AI-driven pipeline with implicit modeling and automated, mesh-free simulation.Speaker BioKarthik Venkatesan is a Lead Engineer in Computational and Digital Product Development at Eaton’s Center for Materials & Manufacturing Innovation in Southfield, Michigan. His work focuses on bridging advanced simulation, AI, and generative design to accelerate the development of next-generation engineered systems. Karthik leads R&D initiatives that span simulation-driven design automation, lightweighting, and digital workflows for both traditional and additive manufacturing (AM) processes.He holds a Ph.D. in Mechanical Engineering from Arizona State University, where he led multiscale modeling efforts for composite materials under DoD- and industry-funded programs. His broader research spans geometry compensation for binder jet AM, performance prediction for polymer extrusion-based AM, virtual design of experiments, and generative AI for material discovery.Karthik is also passionate about computational creativity, with interests spanning astro photography, AI-generated media, and music production 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

Organization:NovineerPresenter:Dr. Ali TamijaniSimulation and Optimization for FFF/FDM Printed PartsPresentation AbstractAdditive manufacturing with FFF/FDM 3D printing has long struggled to optimize toolpaths for better structural performance. Traditional slicing software failed to fully take advantage of material anisotropy, missing opportunities to boost strength and stiffness. Novineer’s toolpath optimization software changes this by maximizing material properties through tailored print paths based on load paths, resulting in a 60% increase in structural stiffness without changing the geometry.Speaker BioDr. Ali Tamijani, the co-founder/CEO of Novineer, is a professor of Aerospace Engineering at ERAU. He has spent three summers at Air Force Research Laboratory (AFRL) as a Faculty Fellow to explore the structural load paths and load flow. This was followed by investigating a Load Path-based Topology Optimization funded by the Air Force Office of Scientific Research (AFOSR)-Young Investigator Program (YIP). Ali is also working on Multiscale Optimization of Additively Manufacturable Cellular Microstructures that received the National Science Foundation (NSF) -CAREER.RECENT INTERVIEWS & ARTICLES* Call for Speakers: CDFAM Barcelona – April 8–9, 2026Flexcompute: Real-Time Computer-Aided OptimizationAcoustic-Driven Computational Design: Premium Branded Audio In The Automotive Industry – Austin Mitchell – Harman InternationalSimulation and Optimization for FFF/FDM Printed Parts – NovineerEngineering Intelligence- Sergey Pigach – CORE studio | Thornton TomasettiAutomating Design Workflows for 3D Concrete Printed Freeform Staircases – Philip Schneider + Timo Zollner 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

Recorded at CDFAM Computational Design Symposium, NYC, October 29-30, 2025https://cdfam.com/nyc-2025/Organization:Alloy EnterprisesPresenter:Ryan O’HaraShaping Flow: Computational Design Strategies for High-Performance Liquid Heat ExchangersPresentation AbstractAt Alloy Enterprises, we combine traditional CAD, implicit geometry modeling, and advanced simulation workflows to engineer high-performance cold plates tailored to the unique thermal and dimensional requirements of each customer. Our approach begins with a curated library of optimized, periodic internal geometries that serve as a foundation for thermal performance and manufacturability. Using computational design tools, we scale and adapt these geometries through parametric controls and implicit modeling techniques, enabling rapid customization across a wide range of form factors. Simulation-driven iteration ensures that each design meets target pressure drop and heat transfer criteria before it reaches the build stage. This integrated workflow allows us to balance design flexibility, performance, and production efficiency in delivering scalable liquid heat exchangers for demanding applications.Speaker BioI am a results-oriented business development leader with over 20 years of DoD acquisition experience. I have extensive experience in advanced manufacturing, aerospace engineering, and federal contracting. I have a proven track record of driving significant revenue growth and securing substantial funding through strategic proposals and federal contracts. With expertise in technical hardware and software sales, I enable cross-functional collaboration in aerospace application development. My technical experience includes transitioning research and development activities from concept to full-scale production, leveraging advanced design and manufacturing concepts. I have demonstrated success in initiating and developing processes, including the certification of materials, equipment, and procedures that comply with aerospace and maritime standards. 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

Recorded at CDFAM Computational Design Symposium, NYC, October 29-30, 2025https://cdfam.com/nyc-2025/Organization:C-InfinityPresenter:Sai NelaturiAssembly Configuration SpacesPresentation AbstractAll non-trivial hardware products are assembled. They are also designed and manufactured in multiple configurations to serve diverse customer needs. Product designs define a configuration space of options that can be instantiated into variants per customer order. OEMs seek to maximize reuse of subassemblies across this space to balance flexibility with cost efficiency—especially in high-mix, low-volume manufacturing.The challenge is translating a product’s design structure into its assembly process structure: reframing design intent as a sequence of operations executed on the factory floor. In Product Lifecycle Management (PLM) terms, this is the translation from the Engineering Bill of Materials (EBOM, “as-designed”) to the Manufacturing Bill of Materials (MBOM, “as-planned”). EBOM and MBOM are not separate domains, but dual representations of the same configuration. Today this translation is manual and painful.At C-Infinity we are automating this translation and building assembly configuration spaces as a foundation for product design and manufacturing planning. By treating EBOM and MBOM as dual views of one structured space, we strengthen reuse, change propagation, streamline configuration management, and enable tighter digital-to-physical integration—addressing long-standing challenges at the heart of advanced manufacturing competitiveness.Speaker BioPh.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. 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

Recorded at CDFAM Computational Design Symposium, NYC, October 29-30, 2025https://cdfam.com/nyc-2025/OrganizationCarbonPresenter:Andrew SinkPodium Performance: The Future is PersonalPresentation AbstractIn this presentation, learn how world-renowned saddle manufacturer, fizik, has embraced the latest in computational design, customization automation and advanced manufacturing to offer cyclists– from amateur to elite– a one-of-a-kind 3d printed saddle, tuned to their specific needs.The One-to-One saddle leverages each partner’s expertise– fizik’s dedication to saddle craftsmanship, Carbon’s groundbreaking lattice design automation and printing technology, and gebioMized’s dynamic pressure mapping precision– to create a saddle that is not only tuned to custom to each rider, but is also fit for champions. In 2025, Tadej Pogačar rode victorious over the Tour de France finish line on a fully custom One-to-One saddle.But podium performance isn’t achieved overnight. In this presentation, we’ll share how we worked to identify the base saddle geometry, developed robust stress testing, and built a custom pipeline to produce this groundbreaking custom bike saddle at scale.Speaker BioAndrew Sink is a Senior Applications Engineer at Carbon and is currently focused on enabling companies to create the next generation of production 3D printed parts at scale. An enthusiastic voice in the additive manufacturing industry, Andrew is always excited to talk about what the future holds for this technology.In addition to his work at Carbon, Andrew has written and published software tools that are designed for home and hobbyist 3D printing as well as various technical guides and videos related to additive manufacturing. After graduating from the University of South Florida with a degree in Technical Communications, Andrew has had feature articles published in traditional print media and has also created a YouTube channel focused on 3D printing that currently has a view count of over 9.5 million. 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

Recorded at CDFAM Computational Design Symposium, NYC, October 29-30, 2025https://cdfam.com/nyc-2025/OrganizationMoon Rabbit Lab X PUMAPresenter:Jesus Marini ParissiRunning Revolution: Computational Design Behind Fast-R NITRO Elite 3Presentation AbstractThe Fast-R NITROTM Elite 3 marks a true performance revolution, combining cutting-edge engineering with data-driven design. As part of the Collaboration with PUMA, Moon Rabbit Lab developed a computational design workflow that integrates digital simulation, biomechanical analysis and advanced optimization techniques that combined different KPI’s of the shoe’s performance before the first prototype was even made.By running several virtual iterations and hundreds of simulation hours, we achieved a 30 % weight reduction alongside a 3.15 % improvement in running economy versus the previous model, gains that translate directly into seconds shaved off personal bests. This approach unites creative engineering, deep knowledge in material science and targeted biomechanical data, with computational design as the central force driving each decision.This case study highlights the power of combining different areas of expertise with computational design at its core. By prioritizing digital testing and optimization, the process reduces errors and minimizes the need for physical prototyping.Beyond footwear, this scalable framework has broad potential across athletic performance products and a wider range of data-driven consumer goods.Speaker BioJesus Marini Parissi is a computational design engineer who merges creative design with advanced engineering methods. He holds a MSc (Master of Science) of Design Engineering from Politecnico di Milano and BSc (Bachelor of Science) in Mechatronics Engineering from Universidad Nacional Autonoma de Mexico, and his portfolio spans performance engineering, consumer goods, automotive product development, and experimental research.He has contributed to global innovation programs like Stanford ME310 and the MIT Design Lab, and worked at Ford Motor Company, earning four patents. He also consulted for brands such as PUMA and Samsung Research America, helping to establish their first Computational Design department.Today, he leads Moon Rabbit Lab, pioneering new frontiers in product development, system optimization, and design research. By fusing imagination with technical expertise, he fosters collaborative innovation and shapes the future of computational design. 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

Organization:Not a RobotPresenter:Matt ShomperRecorded at CDFAM Computational Design Symposium, NYC, October 29-30, 2025https://cdfam.com/nyc-2025/Intelligent Anatomic Models from CT Utilizing MLPresentation AbstractThis presentation discusses an accessible system that takes CT scans and automatically turns them into detailed 3D models while intelligently tagging important anatomical features. Instead of engineers and researchers spending hours manually creating these models and identifying landmarks, our approach uses machine learning to do the heavy lifting.The process works by feeding CT scan data through specialized algorithms that can recognize the structures and convert the flat scan slices into three-dimensional representations. At the same time, the system automatically identifies and labels key anatomical points like bone structures or tissue edges – creating a smart, annotated 3D map of what was scanned.This has the ability to dramatically speed up workflows that previously required tedious manual work. The automated tagging means that medical professionals get consistent, standardized labels across different cases, which is especially valuable for surgical planning and patient-specific implants.The presentation will cover some challenges of utilizing M/L, how manual inputs can train algorithms over time, and looking towards the future of validating such systems for true use in commercialized systems.Speaker BioMatthew is a visionary leader in the computational design of advanced 3D-printed medical implants, with close to 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. 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

Organization:Carnegie Mellon UniversityPresenter:Chris McCombRecorded at CDFAM Computational Design Symposium, NYC, October 29-30, 2025https://cdfam.com/nyc-2025/AI and the Battle for the Soul of DesignPresentation AbstractArtificial intelligence is reshaping the landscape of design and additive manufacturing, accelerating creative workflows while challenging long-held assumptions about authorship, originality, and human intuition. As AI becomes more deeply embedded in computational design tools, it offers unprecedented capabilities for exploration, optimization, and customization—often revealing solutions that elude traditional design methods. Yet this power comes with profound questions: What does it mean to design when machines generate ideas? How do we preserve the human element in a process increasingly influenced by algorithmic reasoning? This presentation examines emerging patterns in AI-driven design, the shifting role of the designer, and the ethical dilemmas that arise when intelligence—natural and artificial—co-create. Through examples from additive manufacturing and beyond, it offers a vision for navigating this new design frontier without losing sight of the creative soul at its core.Speaker BioChris McComb is the Gerard G. Elia Associate Professor of Mechanical Engineering at Carnegie Mellon University. His lab, the Design Research Collective, advances interdisciplinary design research by merging perspectives from engineering, manufacturing, psychology, and computer science. He also serves as the Director of the Human+AI Design Initiative, an interdisciplinary and international group of researchers focused on application of human-AI collaboration to design, with support by industry partners. He is affiliated with the NextManufacturing Center, the Manufacturing Future Institute, and the Wilton E. Scott Institute for Energy Innovation. His research interests include human social systems in design and engineering; machine learning for engineering design; human-AI collaboration and teaming; computation for advanced manufacturing; and STEM education. He received dual B.S. degrees in civil and mechanical engineering from California State University-Fresno. He later attended Carnegie Mellon University as a National Science Foundation Graduate Research Fellow, where he obtained his M.S. and Ph.D. in mechanical engineering. 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

OrganizationHDR IncPresenters:Matthew Goldsberry & Junling ZhuangRecorded at CDFAM Computational Design Symposium, NYC, October 29-30, 2025https://cdfam.com/nyc-2025/Presentation AbstractWhile AI is often used for visualization in architecture, its potential to directly generate and shape geometry within the design process is still emerging. This presentation explores how we have been integrating model-aware AI agents into our design process.We begin with Synthesizer, a custom browser-based modeling tool paired with an Arduino-powered physical controller. Through a simple physical controller, designers trigger higher-order parametric actions, making the act of modeling feel more performative than procedural. Our early beta experiments, focused on building minimal, controller-driven interfaces, explore new possibilities beyond the traditional mouse and keyboard.We then introduce Form Jamming, a method developed within our RhinoMCP workflow. It treats the initial burst of AI-generated geometry as provisional material—something to be shaped and refined into architecture through intentional, iterative moves. While still experimental, this approach has shown promising results in several recent projects, a few of which we will share.This work outlines a new model of computational authorship in which designers and AI agents collaborate through structured dialogue. It points toward a future where generative design is not only more contextual and adaptive but also legible, editable, and deeply integrated into the design process through natural language interaction.Speaker BioMatthew GoldsberryMatt oversees the applied research and implementation of advanced computational design workflows. He is the director of Data-Driven Design and is responsible for developing new computational tools and workflows to facilitate design exploration, automated analysis, and advanced data management. Matt is also a Lecturer at the University of Nebraska-Lincoln, where he teaches courses on advanced geometry and building information modeling. Matt holds a Master of Architecture degree from the University of California Los Angeles and a Bachelor of Science in Architecture degree from the University of Nebraska-Lincoln.Junling ZhuangJunling is a design technologist bridging research and practice in the AEC industry. As a software engineer at HDR’s Data-Driven Design team, he develops AI-powered 3D tools. Junling holds an M.S. in Computational Design from Columbia and is pursuing an M.S. in Computer Science at Georgia Tech. His work has appeared in ACADIA and CAADRIA, and he reviews for top venues including ACADIA, CAADRIA, TAD, and FoA 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

Organization:Cornell UniversityPresenter:Tiffany ChengBioinspired And Biobased 4D-Printing For Adaptive Building FacadesPresentation AbstractWhat if our buildings and products could be manufactured and operated the way biological systems grow and adapt? As an alternative to conventional construction and manufacturing, I will present a bioinspired approach to making through material programming and 4D-printing. By integrating material, structure, and function, plants change shape over varying spatial-temporal scales in response to external stimuli. I will introduce how computational fabrication enable the bioinspired interplay of cellulosic materials, mesostructures, and adaptive motions to create hygromorphic systems powered by the environment. The developed methods are transferable across scales and applications – from hobbyist 3D-printers to industrial robot platforms and self-adjusting wearables for the body to weather-responsive shading in buildings. Through integrative technologies and interdisciplinary solutions, we can leverage biobased materials and bioinspired design principles to create a built environment that is transformative and resilient.Interview: Bioinspired and Biobased 4D-Printing for Adaptive Building Facades – Tiffany ChengTiffany Cheng is a Taiwanese American designer and builder whose work examines the performance potential of natural and biobased materials for smarter and more sustainable forms of making. As Assistant Professor at Cornell University’s Department of Design Tech, Tiffany directs the MULTIMESO Lab to develop computational fabrication processes for creating bioinspired systems across scales, from self-forming furniture to adaptive building components.Previously, Tiffany was Research Group Leader at the Institute for Computational Design and Construction (ICD) at the University of Stuttgart, where she led the Material Programming research group and earned her Doctorate in Engineering. Tiffany holds a Master in Design Studies (Technology) from Harvard University and a Bachelor of Architecture from the University of Southern California. 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

Organization:Key WardPresenter:Asparuh StoyanovBuilding Surrogate Models for Physics Simulation using a No-Code ApproachPresentation AbstractThis project demonstrates a no-code methodology for building surrogate models for engineering simulation. Using such methods, physics simulation analysts can tap seamlessly into the potential of surrogate models, transforming traditional simulation workflows to be more efficient and flexible. In this abstract, we present a workflow of how to use simulation result data to build a 3D surrogate model that any analyst can utilize without requiring programming skills—enhancing the usability of AI-driven simulation tools for broader adoption.Finite Element Method (FEM) simulations are often computationally intensive and challenging to scale, especially for complex structural applications. Our methodology minimizes these resource-heavy processes with a graph-based surrogate model optimized for computational efficiency. To achieve this, we utilized automated extract, transform, and load (ETL) workflows to process the raw simulation data into a shape and format suitable for AI ingestion. We show how, through no-code data processing automation, analysts can focus on deriving insights rather than getting lost in technical details.The dataset used comprised linear static analysis results of a Press Bench model, performed using SOLIDWORKS Simulation. Parametric variables included back height, feet width, and plate length, and the results predicted were displacement and stress. Using data processing and management tools, we first extracted and converted the surface field and volumetric field data, from the original raw format into an open-source “AI-ready” format (. csv,.vtk). This allowed us to gather all simulation data in one place to better understand the data distributions, patterns, and correlations between variables. In the next step, we cleaned the collected data while maintaining different data versions and keeping track of changes. As a final step, using the cleaned and processed dataset, we trained a Graph Neural Network. The model was trained to predict accurate stress and displacement fields within seconds (>90% accuracy), using the 3D volume mesh data as inputs. The whole process from raw data to a trained model took approximately one workday to develop. The same approach will be tested on large deformation nonlinear structural analysis.This project demonstrates how structural simulation data can be used to build surrogate models that accelerate the design process. Advances in AI modeling tools now make these models widely accessible, enabling engineers to leverage physics simulation data without coding or deep machine learning expertise—expanding the possibilities in product design optimization.RECENT INTERVIEWS & ARTICLES* AI Judges in Design: Kristen Edwards – MIT* Manufacturing Driven Design with Rhushik Matroja – CDS* Beyond Surfaces: Applying Intrinsic Geometry Processing in Art and Design: Math Whittaker, New Balance* Maia Zheliazkova – On LightSpray* Design for Additive Manufacturing at CDFAM – Part 2: 2024 Berlin* Design for Additive Manufacturing at CDFAM – Part 1: 2023 NYC 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