AI 4 Engineering
Collection of seminars about applications of AI to engineering problems.
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Seminars
Series
Community
Recent Seminars
Multispecies Wasserstein Gradient Flows
Lauren Conger, California Institute of Technology
Extreme Aerodynamics: Flow Analysis and Control for Highly Gusty Conditions
Kunihiko Taira, University of California, Los Angeles
An intrinsic neuronal manifold underlies brain-wide hierarchical organization of behavior in C. elegans
Charles Fieseler, University of Vienna
Multifidelity, domain decomposition, and stacking for improving training for physics-informed networks
Amanda Howard, Pacific Northwest National Laboratory
Advances in Advancing Interfaces: The Mathematics of Manufacturing of Industrial Foams, Fluidic Devices, and Automobile Painting
James Sethian, University of California, Berkeley
Learning the shape: streamlining data needs in 2D irregular contour parameterization
Ana Larrañaga Janeiro, CINTECX
Statistical mechanics lessons for data-driven methods
Andrei A. Klishin, University of Hawaiʻi at Mānoa
Data Efficient, Robust, and Interpretable Deep Reinforcement Learning for Robotics and Dynamical Systems
Nicolò Botteghi, University of Twente
Enabling Model Reduction of Meshless Nonlocal Methods via Modal Reference Spaces
Steven Rodriguez, United States Naval Research Laboratory
Multi-scale Modeling and Simulations using Digital Twins
Adarsh Krishnamurthy, Iowa State University
Most Watched Seminars
Multifidelity, domain decomposition, and stacking for improving training for physics-informed networks
Amanda Howard, Pacific Northwest National Laboratory
An intrinsic neuronal manifold underlies brain-wide hierarchical organization of behavior in C. elegans
Charles Fieseler, University of Vienna
Extreme Aerodynamics: Flow Analysis and Control for Highly Gusty Conditions
Kunihiko Taira, University of California, Los Angeles
Statistical mechanics lessons for data-driven methods
Andrei A. Klishin, University of Hawaiʻi at Mānoa
Advances in Advancing Interfaces: The Mathematics of Manufacturing of Industrial Foams, Fluidic Devices, and Automobile Painting
James Sethian, University of California, Berkeley
Data Efficient, Robust, and Interpretable Deep Reinforcement Learning for Robotics and Dynamical Systems
Nicolò Botteghi, University of Twente
Learning the shape: streamlining data needs in 2D irregular contour parameterization
Ana Larrañaga Janeiro, CINTECX
Multi-scale Modeling and Simulations using Digital Twins
Adarsh Krishnamurthy, Iowa State University
Enabling Model Reduction of Meshless Nonlocal Methods via Modal Reference Spaces
Steven Rodriguez, United States Naval Research Laboratory
Wall-models of turbulent flows via scientific multi-agent reinforcement learning
H. Jane Bae, California Institute of Technology