Institute for Pure & Applied Mathematics (IPAM) Courses

High-Dimension Perspective on Extracting & Encoding Information in Chemical Systems

Explore high-dimensional data extraction and encoding in complex chemical systems, focusing on exascale computing opportunities, multiscale correlations, and challenges in feature selection and interpretability.

• YouTube
• 53 minutes
• Self-Paced
• Free Video

From Electrons to the Simulation of Materials - IPAM at UCLA

Explore computational workflows for predicting complex materials properties, from phase diagrams to nanostructures, using density functional theory and machine learning potentials.

• YouTube
• 56 minutes
• Self-Paced
• Free Video

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

Explore laboratory automation and complex scientific workflows with ESCALATE software, focusing on experiment planning, data capture, and machine learning integration for halide perovskite synthesis.

• YouTube
• 49 minutes
• Self-Paced
• Free Video

High-Throughput Spectroscopy and Material Discovery by Beyond-DFT Work and Data-Analysis

Explore high-throughput spectroscopy and materials discovery using advanced computational methods, workflows, and data analysis frameworks for accelerated insights in materials science.

• YouTube
• 51 minutes
• Self-Paced
• Free Video

Gaussian Process Approximation & Uncertainty Quantification for Autonomous Experiment

Explore advanced Gaussian process techniques for function approximation, uncertainty quantification, and autonomous experimentation, addressing challenges and improving performance in real-world applications.

• YouTube
• 52 minutes
• Self-Paced
• Free Video

Nonlinear Reduced Basis Methods and Transport in Electronic Structure Calculation

Explore nonlinear reduced basis methods and optimal transport for efficient electronic structure calculations, focusing on a 1D toy model with potential extensions to larger systems.

• YouTube
• 53 minutes
• Self-Paced
• Free Video

Slow Time-Scale Behavior of Fast Microscopic Dynamics - IPAM at UCLA

Explores computational methods for understanding slow time-scale behavior of rapid microscopic dynamics, with applications in materials modeling and plasticity. Presents theory and examples for coarse-graining complex systems.

• YouTube
• 56 minutes
• Self-Paced
• Free Video

Uncertainty-Aware Machine Learning Models of Many-Body Atomic Interactions

Explore symmetry-aware machine learning models for atomic interactions, focusing on E(3)-equivariant neural potentials and Bayesian force fields for improved accuracy in materials modeling.

• YouTube
• 58 minutes
• Self-Paced
• Free Video

Learning Uncertainty-Aware Models of Defect Kinetics at Scale - IPAM at UCLA

Explore advanced techniques for modeling metal properties, including energy landscape exploration, coarse-graining with uncertainty quantification, and descriptor-based approaches for capturing diverse material characteristics.

• YouTube
• 53 minutes
• Self-Paced
• Free Video

Peridynamic Fracture Across Scales - Large Scale Computations With Fast Methods

Explore peridynamic modeling for fracture and damage across scales, enabling large-scale computations with fast convolution-based methods. Learn about new approaches to simulating dynamic fracture and corrosion damage.

• YouTube
• 57 minutes
• Self-Paced
• Free Video

Density Functional Theory - IPAM at UCLA

Comprehensive exploration of density functional theory, covering advanced concepts and applications in materials science, presented by Prof. Vikram Gavini at IPAM's exascale mathematics workshop.

• YouTube
• 1 hour 22 minutes
• Self-Paced
• Free Video

Future Exascale Architectures - IPAM at UCLA

Explore future exascale computing architectures and their applications in materials science, presented by Lawrence Livermore National Laboratory expert Erik Draeger at UCLA's IPAM.

• YouTube
• 1 hour 10 minutes
• Self-Paced
• Free Video

Density Functional Theory - IPAM at UCLA

Comprehensive introduction to density functional theory, exploring its principles and applications in materials science and computational physics.

• YouTube
• 1 hour 30 minutes
• Self-Paced
• Free Video

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

Explore Neur2SP, a novel approach to two-stage stochastic programming using neural networks. Learn how this method efficiently solves complex decision-making problems under uncertainty.

• YouTube
• 50 minutes
• Self-Paced
• Free Video

Reasoning Algorithmically- From Toy Experiments to AGI Modules

Explore neural networks' potential in algorithmic computation, from toy experiments to groundbreaking applications in mathematics and AI reasoning, with insights on their transformative impact on machine learning and computer science.

• YouTube
• 52 minutes
• Self-Paced
• Free Video