Physics Informed Machine Learning
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Samy Wu Fung - Using Hamilton-Jacobi PDEs for Optimization
Physics Informed Machine Learning
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David Bortz - The Surprising Robustness and Computational Efficiency of Weak Form System Identif...
Physics Informed Machine Learning
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Tess Smidt - Symmetry’s made to be broken: Learning how to break symmetry with symmetry-preservin...
Physics Informed Machine Learning
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Daniela Witten - Double dipping: problems and solutions, with application to single-cell RNA-seque..
Physics Informed Machine Learning
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Melanie Weber - Exploiting Geometric Structure in Machine Learning and Optimization
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John Wright - Deep Networks and the Multiple Manifold Problem
Physics Informed Machine Learning
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Jörn Dunkel - Symmetry-informed model inference for active matter
Physics Informed Machine Learning
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Andrew Stuart - Supervised Learning For Operators
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Richard Baraniuk - Deep Network Spline Geometry
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Zhuoyuan Song/June Zhang/Frankie Zhu - Introducing our Colleagues in Hawaii: Updates and Open Challe
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Aditya Nair/Floris van Breugel - Leveraging Machine Learning for interaction-based models and contro
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Dominique Zosso - Graph-Based Geometric Data Analysis
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John Wright - Recent Directions in Nonconvexity: Landscapes, Methods, Architectures
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Hod Lipson - The AI Scientist Automating Discovery, From Cognitive Robotics to Computational Biology
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Shirley Ho - Interpretable Learning in Physical Systems
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Na Li - Scalable and Safe Learning-based Decision-Making in Dynamical Systems
Physics Informed Machine Learning
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Liliana Borcea - Waveform inversion via reduced order modeling
Physics Informed Machine Learning
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Laure Zanna - Data-driven turbulence closures for ocean and climate models: advances and challenges
Physics Informed Machine Learning
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Joan Bruna - Prospects and Challenges of Machine Learning in the Physical World
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Benjamin Peherstorfer - Physics-based machine learning for quickly simulating transport-dominated...
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Jane Bae - Wall-models of turbulent flows via scientific multi-agent reinforcement learning
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Rose Yu - Incorporating Symmetry for Learning Spatiotemporal Dynamics
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AI Institute in Dynamic Systems
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Eva Kanso - One fish, two fish
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Nicholas Zabaras - Physics Informed Learning for Multiscale Dynamical Systems
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Kevin Carlberg - AI for Computational Physics: Toward real-time high-fidelity simulation
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Anima Anandkumar - Neural operator: A new paradigm for learning PDEs
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Beverley McKeon - What’s in a mean (what, how and why)? Towards nonlinear models of wall turbulence
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Tamara Kolda - Practical Leverage-Based Sampling for Low-Rank Tensor Decomposition
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