Podcast cover
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

Episode cover

Cami Collins - Multi-Fidelity Integrated Modeling for Fusion Device Design with FREDA - IPAM at UCLA

04 May 2026
46m
Episode cover

Shantenu Jha - All (Foundation) Models are wrong. Some (Foundation) Models can be made useful.

17 Apr 2026
42m
Episode cover

Alexander Velberg - Data-driven discovery of sub-grid models for collisionless magnetic reconnection

17 Apr 2026
47m
Episode cover

Jingwei Hu - A Neural Score-Based Particle Method for the Vlasov-Maxwell-Landau System

17 Apr 2026
47m
Episode cover

Guannan Zhang - Generative diffusion models learning stochastic flow maps in particle-based sims

17 Apr 2026
45m
Episode cover

Luis Oliveira e Silva - Ab initio PIC simulation/technique for multi-fidelity/reduced plasma models

16 Apr 2026
40m
Episode cover

Benjamin Sanderse - Structure-preserving SciML for discovering ODEs and SDEs in fluid flows

16 Apr 2026
44m
Episode cover

Boris Kramer - Lift & Learn: Scientific ML for nonlinear conservative partial differential equations

16 Apr 2026
57m
Episode cover

Ammar Hakim - BEACONS & BEACONS-FM: Modular, Composable, Formally Verified Fusion Foundation Models

16 Apr 2026
47m
Episode cover

Leonardo Zepeda-Núñez - Learning Turbulent & Chaotic Dynamics via Data-Driven Probabilistic Models

15 Apr 2026
52m
Episode cover

Cristina Rea - Data-driven learning for disruption prevention and performance optimization

15 Apr 2026
47m
Episode cover

Diego Del-Castillo-Negrete - Generative AI methods for turbulence and kinetic computations

15 Apr 2026
44m
Episode cover

Karen Willcox - Multifidelity Proper Orthogonal Decomposition - IPAM at UCLA

15 Apr 2026
47m
Episode cover

Karen Willcox - Learning Structure-exploiting Reduced Models with Operator Inference

15 Apr 2026
56m
Episode cover

Karen Willcox - The Important Role of Mathematics in the Digital Twin Revolution

15 Apr 2026
50m
Episode cover

Jiequn Han - Learning Evolution Operators Across PDE Systems: MetaLearning & TestTime Generalization

15 Apr 2026
45m
Episode cover

Qin Li - Stabilizing plasma instabilities cast as PDE-constrained optimization - IPAM at UCLA

14 Apr 2026
39m
Episode cover

Peter Benner - Discovery and Model Reduction of Hamiltonian Systems - IPAM at UCLA

14 Apr 2026
52m
Episode cover

Nikola Kovachki - Demystifying Data-Driven Probabilistic Medium-Range Weather Forecasting

14 Apr 2026
49m
Episode cover

Jingmei Qiu - Sampling-Based Adaptive Rank Integrators for Multi-scale Kinetic Models

13 Apr 2026
51m
Page 6

Follow this podcast in Podwise

Sign in to get AI summaries, transcripts and mind maps for any episode, including new ones.

Open in Podwise