
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


Mark Tuckerman - Beating viscosity-conductivity inverse relation to create electrolyte for battery

Stefan Wippermann - Capturing Electrode Potential Dynamics in Ab initio Electrochemical Simulations

Clotilde Cucinotta - Advancing Ab Initio Molecular Dynamics for Electrified Interfaces

Jun Cheng - AI acceleration of AIMD simulation of electrochemical interfaces - IPAM at UCLA

Michael Eikerling - Local Reaction in Electrocatalytic Media: Interface Theory, Realism, Complexity

Jörg Neugebauer - Boundary conditions for atomistic simulations: Insights, solutions and challenges

Giulia Galli - Interfaces between water and semiconductors from first principles - IPAM at UCLA

Chong Liu - An experimentalist's (primitive) view of stochasticity in electrochemistry

Roman Grigoriev - Bridging scales using physically-informed machine learning - IPAM at UCLA

Daniel Schwalbe-Koda - Scales in Material Modeling w/ Atomistic Sim, Info Theory, & Generative Model

Yunan Yang - Transport- & Measure-Theoretic Approaches Modeling, Identifying, & Forecasting Systems

Rachel Kurchin - Modeling Electrochemical Reaction Rates at Fluid-Solid Interfaces - IPAM at UCLA

Michael Herbst - Algorithmic differentiation (AD) for plane-wave DFT - IPAM at UCLA

Martin Bazant - Unified quantum theory of electrochemical kinetics by coupled ion-electron transfer

Huan Lei - Energy-stable machine-learning model of non-Newtonian hydrodynamics w/ molecular fidelity

Juergen Fuhrmann - Continuum scale electrolyte simulations based on finite volume methods

Jun Huang - Hybrid density-potential functional approach to bridge atomistic scale and continuum

Manuel Landstorfer - Continuum Thermodynamic Models for Electrochemical Interfaces - IPAM at UCLA

David Bortz - Weak form SciML for Learning Models on Different Scales - IPAM at UCLA
Follow this podcast in Podwise
Sign in to get AI summaries, transcripts and mind maps for any episode, including new ones.
