H.B. Keller Colloquium
Charbel Farhat is the Vivian Church Hoff Professor of Aircraft Structures in the School of Engineering at Stanford University, where he is also a professor in the Institute for Computational and Mathematical Engineering. From 2008 to 2023, he chaired the Department of Aeronautics and Astronautics, serving from 2022 to 2023 as its inaugural James and Anna Marie Spilker Chair. He also directed the Stanford-King Abdulaziz City for Science and Technology Center of Excellence for Aeronautics and Astronautics (2014--2024) and served on multiple national advisory boards, including the Space Technology Industry-Government-University Roundtable (2017--2023), the U.S. Air Force Scientific Advisory Board (2015--2019), and the Bureau of Industry and Security's Emerging Technology and Research Advisory Committee (2008--2018). From 2007 to 2018, he directed the Army High Performance Computing Research Center at Stanford. Recognized by the U.S. Navy as a Primary Key-Influencer, he flew with the Blue Angels during Fleet Week 2014. He holds a Ph.D. in Civil Engineering from the University of California, Berkeley and is a member of three national academies: the National Academy of Engineering, the Royal Academy of Engineering (UK), and the Lebanese Academy of Sciences. His honors include a Vannevar Bush Faculty Fellowship from the Department of Defense and Docteur Honoris Causa degrees from Ecole Normale Superieure Paris-Saclay, Ecole Centrale de Nantes, and Ecole Nationale Superieure d'Arts et Metiers. He is a laureate of the 2024 Kuwait Prize in Applied Sciences, of the TAKREEM AMERICA Foundation for Scientific and Technological Achievement, and an ISI Highly Cited Researcher in Engineering. Farhat is a Fellow of AIAA, ASME, IACM, SES, SIAM, USACM, and WIF. He was knighted in the Order of Academic Palms and awarded the Chevalier Medal by the Prime Minister of France. Among his many distinctions, he has received the Lifetime Achievement Award and the Spirit of St. Louis Medal from the ASME, the Ashley Award for Aeroelasticity, the Structures, Structural Dynamics and Materials Award, the Collier Aerospace HyperX/AIAA Structures Award, and the Journal Authors Seminar Award from the AIAA, the Computational Fluid Dynamics Award from SAE International, the Aurel Stodola Medal from ETH, and the ALERT Geomaterials Medal. From the USACM, he has been awarded the John von Neumann Medal, the Computational and Applied Sciences Award, and the R.H. Gallagher Special Achievement Award. His contributions to computational mechanics have also been recognized with the Gauss-Newton Medal, the IACM Award, the Computational Mechanics Award, and the Young Investigator Award from the IACM. Additionally, he has received the Gordon Bell Prize and the Sidney Fernbach Award from the IEEE Computer Society, the Grand Prize from the Japan Society for Computational Engineering and Science, the Modeling and Simulation Award from the Department of Defense, and the Presidential Young Investigator Award from the National Science Foundation and the White House. From 2014 to 2024, Farhat served as Editor-in-Chief of the International Journal for Numerical Methods in Engineering and, from 2017 to 2024, of the International Journal for Numerical Methods in Fluids. He is currently an Associate Editor of the Journal of Computational Physics and a member of the editorial boards of eight international scientific journals. A frequent AGARD and NATO lecturer, he has delivered keynote and plenary talks at major international conferences. He has authored over 650 refereed publications in fluid-structure interaction, computational fluid dynamics, structural mechanics, acoustics, supercomputing, parallel processing, model order reduction, and physics-based machine learning. His research has been funded by NSF, AFOSR, ONR, ARL, DARPA, NASA, DoE, and various national laboratories and industry leaders, including Autodesk, Boeing, Ford, Lockheed-Martin, Michelin, Toyota, and Volkswagen.
Generative Design (GD) combines artificial intelligence (AI), physics-based modeling, and multi-objective optimization to autonomously explore and refine engineering designs. Despite its promise in aerospace, automotive, and other high-performance applications, current GD methods face critical challenges: AI approaches require large datasets and often struggle to generalize; topology optimization is computationally intensive and difficult to extend to multiphysics problems; and model order reduction for evolving geometries remains underdeveloped. To address these challenges, we introduce a unified, structure-preserving framework for GD based on optimal transport (OT), enabling simultaneous interpolation of complex geometries and their associated physical solution fields across evolving design spaces, even with non-matching meshes and substantial shape changes. This capability leverages Gaussian splatting to provide a continuous, mesh-independent representation of the solution and Wasserstein barycenters to enable smooth, mathematically "mass"-preserving blending of geometries, offering a major advance over surrogate models tied to static meshes. Our framework efficiently interpolates positive scalar fields across arbitrarily shaped, evolving geometries without requiring identical mesh topology or dimensionality. OT also naturally preserves localized physical features—such as stress concentrations or sharp gradients—by conserving the spatial distribution of quantities, interpreted as "mass" in a mathematical sense, rather than averaging them, avoiding artificial smoothing. Preliminary extensions to signed and vector fields are presented. Representative test cases demonstrate enhanced efficiency, adaptability, and physical fidelity, establishing a foundation for future foundation-model-powered generative design workflows.
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