Videos

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Data-efficient kernel methods for learning differential equations and their solution operators Thumbnail

Data-efficient kernel methods for learning differential equations and their solution operators

Presenter
  • Bamdad Hosseini
October 8, 2025
IMSI

Transfer learning in deep operator networks

Presenter
  • Somdatta Goswami
June 7, 2023
ICERM

Learning to Predict using Network of Spiking Neurons

Presenter
  • Biswadeep Chakraborty
June 8, 2023
ICERM
Haomin Zhou - A supervised learning scheme for Hamilton-Jacobi equation via density coupling Thumbnail

Haomin Zhou - A supervised learning scheme for Hamilton-Jacobi equation via density coupling

Presenter
  • Haomin Zhou
July 15, 2025
IPAM
Stan Osher - A Characteristic-Based Deep Learning Framework for Hamilton–Jacobi Equations with Application to Optimal Transport Thumbnail

Stan Osher - A Characteristic-Based Deep Learning Framework for Hamilton–Jacobi Equations with Application to Optimal Transport

Presenter
  • Stanley Osher
July 14, 2025
IPAM
A phase-space perspective on scientific machine learning Thumbnail

A phase-space perspective on scientific machine learning

Presenter
  • Ivan Dokmanic
June 11, 2025
IMSI
David Bortz - Weak form SciML for Learning Models on Different Scales - IPAM at UCLA Thumbnail

David Bortz - Weak form SciML for Learning Models on Different Scales - IPAM at UCLA

Presenter
  • David Bortz
October 7, 2025
IPAM
Jiajia Yu - Learning and Inference in Mean-Field Games - IPAM at UCLA Thumbnail

Jiajia Yu - Learning and Inference in Mean-Field Games - IPAM at UCLA

Presenter
  • Jiajia Yu
July 16, 2025
IPAM

Learning Neural Operators for Complex Physical System Modeling

Presenter
  • Yue Yu
June 9, 2023
ICERM

GFlowNets to accelerate scientific discovery with machine learning

Presenter
  • Alex Hernandez-Garcia
June 6, 2023
ICERM
How to assess scientific machine learning models? Prediction errors and predictive uncertainty quantification Thumbnail

How to assess scientific machine learning models? Prediction errors and predictive uncertainty quantification

Presenter
  • Matthias Rupp
November 21, 2019
IPAM
Michele Ceriotti - Machine learning for atomic-scale modeling - potentials and beyond - IPAM at UCLA Thumbnail

Michele Ceriotti - Machine learning for atomic-scale modeling - potentials and beyond - IPAM at UCLA

Presenter
  • Michele Ceriotti
May 3, 2023
IPAM