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AI/ML+Physics Part 1: Choosing what to model [Physics Informed Machine Learning]
Surrogate Model Based Optimization and Active Learning for HPC Applications -- Juliane Mueller
Data-driven Surrogate and Model discovery for Multi-physics simulation
Generative modeling of molecular dynamics trajectories | Bowen Jing and Hannes Stärk
Modeling Protein Evolution with Generative Models:from Extant Sequence Data to Evolutionary Dynamics
A surrogate modeling journey through Gaussian processes
Romit Maulik: Incorporating inductive biases for the surrogate modeling of dynamical systems
Gravitational waveform modeling with physics informed neural networks and surrogates - Nils Deppe
Emulation Strategies for Epidemic Agent-Based Models
A Sharp Flexibility/Rigidity Result for the Stationary Isotropic Landau Equation | Nicholas Gismondi
AJS - Youngsoo Choi - Where are we with data-driven surrogate modeling for physical simulations
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Last Updated: September 24, 2026
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In this High-Affinity Talk, Konrad Krawczyk, founder of NaturalAntibody, challenges the hype around AI "silver bullets" in biologics ... Full episode, transcript and resources: ... FirstPrinciples Talks presents Shallow Recurrent Decoders for the Automated Discovery of Physical This video discusses the first stage of the machine learning process: (1) formulating a problem to Bowen Jing (MIT) and Hannes Stärk (MIT) discuss their recent work on MDGEN - a generative Speaker: Prof Martin Weigt from University of Sorbonne. Industrial Statistics Section of ISBA: If you would to join the Industrial Statistics section of ISBA, you may do so here: ... Date: Wed Apr 14, 16:00 - 17:30 UK time. Speaker: Romit Maulik Title: Incorporating inductive biases for the Institute for Advanced Study Astrophysics Seminar Topic: Gravitational waveform In infectious disease epidemiology, agent-based Speaker: Nicholas Gismondi Affiliation: Purdue University Title: A Sharp Flexibility/Rigidity Result for the Stationary Isotropic ...
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