Videos

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Physics-Inspired Machine Learning Methods: A Status Report on Predictive Chemistry Thumbnail

Physics-Inspired Machine Learning Methods: A Status Report on Predictive Chemistry

Presenter
  • Teresa Head-Gordon
April 8, 2024
IMSI

Perspectives and Practice for Scientific Machine Learning in Process Systems: Dynamic Surrogacy and Causal Discovery

Presenter
  • Jordan Jalving
March 24, 2024
ICERM

Physics Constrained Machine Learning for Scientific Computing

Presenter
  • Danielle Robinson
March 23, 2024
ICERM
Efficient Invariant Embeddings for multisets, point sets, and graphs Thumbnail

Efficient Invariant Embeddings for multisets, point sets, and graphs

Presenter
  • Nadav Dym
December 13, 2023
IMSI
Nathan Wiebe - Quantum Machine Learning - IPAM at UCLA Thumbnail

Nathan Wiebe - Quantum Machine Learning - IPAM at UCLA

Presenter
  • Nathan Wiebe
October 16, 2023
IPAM

A Method for Computing Inverse Parametric PDE Problems with Randomized Neural Networks

Presenter
  • Suchuan Dong
June 9, 2023
ICERM

Learning Neural Operators for Complex Physical System Modeling

Presenter
  • Yue Yu
June 9, 2023
ICERM

Optimization-in-the-loop ML for energy and climate

Presenter
  • Priya Donti
June 9, 2023
ICERM

Learning to Predict using Network of Spiking Neurons

Presenter
  • Biswadeep Chakraborty
June 8, 2023
ICERM

Evolving Spiking Neural Networks for Scientific Applications

Presenter
  • Catherine Schuman
June 8, 2023
ICERM

A Probabilistic Future for Neuromorphic Computing

Presenter
  • Brad Aimone
June 8, 2023
ICERM

Dynamics and symmetries in neural network learning

Presenter
  • Stefanie Jegelka
June 8, 2023
ICERM