
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


Steve Cowley - Multi-scale Plasma Turbulence — the key to fusion power - IPAM at UCLA

IPAM "Quantum Topology, Character Varieties and Low-Dimensional Geometry" Fall 2026 Program Overview

Tomo-Hiko Watanabe - Multi-scale turbulence and isotope effects on the trapped electron mode

Genia Vogman - Anomalous transport in pulsed power, continuum kinetic sim, reduced transport models

Ammar Hakim - On Constructing Numerical Schemes for a Hierarchy of Fusion Plasma Problems

Anirban Chaudhuri - Multifidelity surrogates and decision-making for digital twins - IPAM at UCLA

Maximilian Ruth - A Statistical Magnetohydrodynamic Equilibrium Model - IPAM at UCLA

Alex Gorodetsky - Generalized structural exploitation in multi-fidelity sampling and learning

David Hatch - Bridging the pedestal gap: intermediate-fidelity models for pedestal transport

Gabriele Merlo - Multi-fidelity approach to multiscale nature of plasma turbulence in stellarators

Ben Zhu - A multi-fidelity approach in boundary plasma modeling - IPAM at UCLA

Bryan Reuter - Enhancing Particle-Based Kinetic Methods Through Multifidelity Discretizations

Kentaro Hara - Multi-fluid, high-order moment models for partially ionized plasmas - IPAM at UCLA

Amanda Howard - Multifidelity, domain decomposition, stacking for training physics-informed networks

Elizabeth Qian - Multifidelity control variates for supervised learning - IPAM at UCLA

Victor Artigues - Plasma Turbulence in TensorFlow: Reduced Models and Optimization - IPAM at UCLA

Andrew Christlieb - An introduction to Scientific Machine Learning - IPAM at UCLA

Ionut-Gabriel Farcas - A tutorial on multi-fidelity methods for fusion energy research, Pt. 2/2

Ionut-Gabriel Farcas - A tutorial on multi-fidelity methods for fusion energy research, Pt. 1/2
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