Institute for Pure & Applied Mathematics (IPAM)

Institute for Pure & Applied Mathematics (IPAM)

Latest Videos

Quantum Computing & Quantum Chemistry - Wes Campbell of UCLA Physics

Quantum Computing & Quantum Chemistry - Wes Campbell of UCLA Physics

Switching Disciplines in Graduate Science - Wes Campbell of UCLA Physics

Switching Disciplines in Graduate Science - Wes Campbell of UCLA Physics

Stan Moore - Optimizing GPU Performance Case Study Using Chain Benchmark in LAMMPS

Stan Moore - Optimizing GPU Performance Case Study Using Chain Benchmark in LAMMPS

Justin Smith - The state of neural network interatomic potentials - IPAM at UCLA

Justin Smith - The state of neural network interatomic potentials - IPAM at UCLA

Richard Hennig & Jason Gibson - AI-driven workflows for the discovery of novel superconductors

Richard Hennig & Jason Gibson - AI-driven workflows for the discovery of novel superconductors

James Corbett - Flux a next generation resource manager for HPC and beyond - IPAM at UCLA

James Corbett - Flux a next generation resource manager for HPC and beyond - IPAM at UCLA

Juliane Mueller - Adaptive Computing and multi-fidelity learning - IPAM at UCLA

Juliane Mueller - Adaptive Computing and multi-fidelity learning - IPAM at UCLA

Michele Ceriotti - Machine learning for atomic-scale modeling - potentials and beyond - IPAM at UCLA

Michele Ceriotti - Machine learning for atomic-scale modeling - potentials and beyond - IPAM at UCLA

Aurora Clark - high-dimension perspective on extracting & encoding information in chemical systems

Aurora Clark - high-dimension perspective on extracting & encoding information in chemical systems

Ralf Drautz - From electrons to the simulation of materials - IPAM at UCLA

Ralf Drautz - From electrons to the simulation of materials - IPAM at UCLA

Joshua Schrier - Creating Complex Scientific Workflows that Reach into the Real World - IPAM at UCLA

Joshua Schrier - Creating Complex Scientific Workflows that Reach into the Real World - IPAM at UCLA

Samuel Blau - High-Throughput DFT and Monte Carlo for Reaction Networks and Machine Learning

Samuel Blau - High-Throughput DFT and Monte Carlo for Reaction Networks and Machine Learning

Amit Acharya - Slow time-scale behavior of fast microscopic dynamics - IPAM at UCLA

Amit Acharya - Slow time-scale behavior of fast microscopic dynamics - IPAM at UCLA

Frederic Legoll - Parareal algorithms for molecular dynamics simulations - IPAM at UCLA

Frederic Legoll - Parareal algorithms for molecular dynamics simulations - IPAM at UCLA

Boris Kozinsky - Uncertainty-aware machine learning models of many-body atomic interactions

Boris Kozinsky - Uncertainty-aware machine learning models of many-body atomic interactions

Marisol Koslowski - Surrogate Hot-spot Models for Simulations of Detonation in Energetic Materials

Marisol Koslowski - Surrogate Hot-spot Models for Simulations of Detonation in Energetic Materials

Thomas Swinburne - Learning uncertainty-aware models of defect kinetics at scale - IPAM at UCLA

Thomas Swinburne - Learning uncertainty-aware models of defect kinetics at scale - IPAM at UCLA

James Kermode - Multiscale and data-driven methods for the simulation of material failure

James Kermode - Multiscale and data-driven methods for the simulation of material failure

Christoph Ortner - Atomic Cluster Expansion with and Without Atoms - IPAM at UCLA

Christoph Ortner - Atomic Cluster Expansion with and Without Atoms - IPAM at UCLA

Robert Lipton - Fracture as an emergent phenomenon - IPAM at UCLA

Robert Lipton - Fracture as an emergent phenomenon - IPAM at UCLA

Maria Emelianenko - Integrating multiscale materials modeling w interpretable automation techniques

Maria Emelianenko - Integrating multiscale materials modeling w interpretable automation techniques

Yekaterina Epshteyn - Multiscale modeling and analysis of grain growth in polycrystalline materials

Yekaterina Epshteyn - Multiscale modeling and analysis of grain growth in polycrystalline materials

$Florin Bobaru - Peridynamic fracture across scales large scale computations with fast methods$

Florin Bobaru - Peridynamic fracture across scales large scale computations with fast methods

Lev Truskinovsky - Mesoscopic tensorial model of crystal plasticity - IPAM at UCLA

Lev Truskinovsky - Mesoscopic tensorial model of crystal plasticity - IPAM at UCLA

Shenglin Huang - Data-Driven Model Discovery for Non-equilibrium Processes - IPAM at UCLA

Shenglin Huang - Data-Driven Model Discovery for Non-equilibrium Processes - IPAM at UCLA

Xiantao Li - A stochastic algorithm for self-consistent calculations in DFT - IPAM at UCLA

Xiantao Li - A stochastic algorithm for self-consistent calculations in DFT - IPAM at UCLA

Gabor Csányi - Machine learning potentials from polynomials to message passing networks

Gabor Csányi - Machine learning potentials from polynomials to message passing networks

Vasily Bulatov - What are we going to do with data generated in exascale simulations? - IPAM at UCLA

Vasily Bulatov - What are we going to do with data generated in exascale simulations? - IPAM at UCLA

Steve Fitzgerald - Path integral formulation of stochastic processes... - IPAM at UCLA

Steve Fitzgerald - Path integral formulation of stochastic processes... - IPAM at UCLA

Laura Grigori - Randomization techniques for solving large scale linear algebra problems

Laura Grigori - Randomization techniques for solving large scale linear algebra problems

Vikram Gavini - Fast Accurate and Large-scale Ab-initio Calculations for Materials Modeling

Vikram Gavini - Fast Accurate and Large-scale Ab-initio Calculations for Materials Modeling

David Bowler - Large-scale and linear scaling DFT why we need it and how we do it - IPAM at UCLA

David Bowler - Large-scale and linear scaling DFT why we need it and how we do it - IPAM at UCLA

Jaafar El-Awady - dislocation in high thermomechanical condition in Additive Manufacturing of Alloys

Jaafar El-Awady - dislocation in high thermomechanical condition in Additive Manufacturing of Alloys

Aidan Thompson - LAMMPS simulation physics models machine-learning potentials exascale computing

Aidan Thompson - LAMMPS simulation physics models machine-learning potentials exascale computing

Lin Lin - Interacting models for twisted bilayer graphene quantum chemistry approach - IPAM at UCLA

Lin Lin - Interacting models for twisted bilayer graphene quantum chemistry approach - IPAM at UCLA

Thomas Hudson - Multiscale Modeling - IPAM at UCLA

Thomas Hudson - Multiscale Modeling - IPAM at UCLA

Vikram Gavini - DFT 2 - Density functional theory - IPAM at UCLA

Vikram Gavini - DFT 2 - Density functional theory - IPAM at UCLA

Tim Germann - Molecular Dynamics 1 - IPAM at UCLA

Tim Germann - Molecular Dynamics 1 - IPAM at UCLA

Vikram Gavini - DFT 1 - Density functional theory - IPAM at UCLA

Vikram Gavini - DFT 1 - Density functional theory - IPAM at UCLA

Tina Eliassi-Rad - The Pitfalls of Using ML-based Optimization - IPAM at UCLA

Tina Eliassi-Rad - The Pitfalls of Using ML-based Optimization - IPAM at UCLA

Rahul Mazumder - Discrete Optimization-aided Structured Learning at Scale - IPAM at UCLA

Rahul Mazumder - Discrete Optimization-aided Structured Learning at Scale - IPAM at UCLA

Elias Khalil - Neur2SP Neural Two-Stage Stochastic Programming - IPAM at UCLA

Elias Khalil - Neur2SP Neural Two-Stage Stochastic Programming - IPAM at UCLA

Petar Veličković - Reasoning Algorithmically from Toy Experiments to AGI Modules - IPAM at UCLA

Petar Veličković - Reasoning Algorithmically from Toy Experiments to AGI Modules - IPAM at UCLA

Tias Guns - Prediction + Optimisation without and with decision-focused learning - IPAM at UCLA

Tias Guns - Prediction + Optimisation without and with decision-focused learning - IPAM at UCLA

Timo Berthold - Machine Learning inside MIP solvers - IPAM at UCLA

Timo Berthold - Machine Learning inside MIP solvers - IPAM at UCLA

Phebe Vayanos - Integer optimization for predictive & prescriptive analytics in high stakes domains

Phebe Vayanos - Integer optimization for predictive & prescriptive analytics in high stakes domains

Bartolomeo Stellato - Learning for Decision-Making Under Uncertainty - IPAM at UCLA

Bartolomeo Stellato - Learning for Decision-Making Under Uncertainty - IPAM at UCLA

Pascal Van Hentenryck - Fusing Machine Learning and Optimization - IPAM at UCLA

Pascal Van Hentenryck - Fusing Machine Learning and Optimization - IPAM at UCLA

Priya Donti - Optimization-in-the-loop AI for energy and climate - IPAM at UCLA

Priya Donti - Optimization-in-the-loop AI for energy and climate - IPAM at UCLA

Yuandong Tian - AI-guided nonlinear optimization for real-world problems - IPAM at UCLA

Yuandong Tian - AI-guided nonlinear optimization for real-world problems - IPAM at UCLA