Institute for Pure & Applied Mathematics (IPAM) · Science

Institute for Pure & Applied Mathematics (IPAM)

The Institute for Pure and Applied Mathematics (IPAM) is a National Science Foundation Math Institute at the University of California, Los Angeles (UCLA) that fosters the interaction of mathematics with a broad range of science and technology, builds new interdisciplinary research communities, promotes mathematical innovation, and engages and transforms the world through mathematics. IPAM fulfills its mission through workshops and programs that connect mathematics and other disciplines or multiple areas of mathematics. These activities bring in thousands of visitors annually from academia, government, and industry. The NSF has encouraged IPAM to make the scientific presentations of our speakers available to a wider audience. While this YouTube channel contains many of our most popular videos, our full video archive is available at www.ipam.ucla.edu/videos.

Episodes

Lucas Wagner - Compact representations of excited states from QMC, as data for low-energy models

02 Jun 2022

51m

Mark Tuckerman - Quantum time correlation functions in an open-chain path integral distribution

02 Jun 2022

53m

Ilyes Batatia - Unified understanding of E(3)-Equivariant Interatomic Potentials Theory/Applications

01 Jun 2022

43m

David Ceperley - Quantum Monte Carlo and Machine Learning Simulations of Dense Hydrogen

01 Jun 2022

50m

Robert Webber - Approximate matrix eigenvalues, subspace iteration w/ repeated random sparsification

Institute for Pure & Applied Mathematics (IPAM)

Episodes

Lucas Wagner - Compact representations of excited states from QMC, as data for low-energy models

Mark Tuckerman - Quantum time correlation functions in an open-chain path integral distribution

Ilyes Batatia - Unified understanding of E(3)-Equivariant Interatomic Potentials Theory/Applications

David Ceperley - Quantum Monte Carlo and Machine Learning Simulations of Dense Hydrogen

Robert Webber - Approximate matrix eigenvalues, subspace iteration w/ repeated random sparsification

Julia Westermayr - Physically inspired machine learning for excited states - IPAM at UCLA

Michael Scherbela - High accuracy wavefunctions using deep-learning-based variational Monte Carlo

Kieron Burke - Conditional probability density functional theory - IPAM at UCLA

Matthew Foulkes - Approximating Many-Electron Wave Functions using Neural Networks - IPAM at UCLA

Anouar Benali - multideterminant many-body methods to describe ground and excited state properties

Jan Hermann - Neural-network wave functions for quantum chemistry - IPAM at UCLA

Juan Carrasquilla - Variational Neural Annealing - IPAM at UCLA

Sandeep Sharma - Auxiliary field quantum Monte Carlo (AFQMC) with multi Slater wavefunctions

Raymond Clay - Machine Learning in Equation of State and Transport Modeling at Extreme Conditions

Jonathan Weare - A fast Jacobi iteration with repeated random sparsification - IPAM at UCLA

Eran Rabani - Stochastic Density Functional Theory - IPAM at UCLA

Leslie Vogt-Maranto - Molecular Electron Densities via Machine Learning - IPAM at UCLA

Claudia Filippi - Targeting excited states with quantum Monte Carlo - IPAM at UCLA

Giuseppe Carleo - Fermionic neural-network quantum states - IPAM at UCLA

Shiwei Zhang - Auxiliary-field methods for quantum materials - IPAM at UCLA