
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


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

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

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

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

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

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

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

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

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

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

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

Karen Willcox - Multifidelity Proper Orthogonal Decomposition - IPAM at UCLA

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

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

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

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

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

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

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