Institute for Pure & Applied Mathematics (IPAM) Courses

Adaptive Computing and Multi-Fidelity Learning - IPAM at UCLA

Explore adaptive computing and multi-fidelity learning for efficient optimization and uncertainty quantification in complex scientific workflows, with applications and scaling challenges discussed.

• YouTube
• 49 minutes
• On-Demand
• Free Video

ExaFEL - Achieving Real-Time XFEL Data Analysis Using Exascale Hardware

Explore real-time data analysis for X-ray Free Electron Laser experiments using high-performance computing, focusing on challenges, workflow design, and performance insights for future scientific applications.

• YouTube
• 52 minutes
• On-Demand
• Free Video

Scaling NumPy Applications from 1 CPU to Thousands of GPUs - IPAM at UCLA

Discover cuNumeric: a NumPy replacement enabling Python applications to scale across multiple nodes and leverage GPU acceleration for enhanced performance and dataset handling.

• YouTube
• 37 minutes
• On-Demand
• Free Video

Transparent, Reproducible, and Adaptable Data Analysis with Snakemake

Explore Snakemake for sustainable data analysis, enabling transparency, reproducibility, and adaptability in scientific workflows and computational research.

• YouTube
• 38 minutes
• On-Demand
• Free Video

X-Ray Micro-Tomography at the Advanced Light Source and NERSC Superfacility

Explore X-ray micro-tomography at ALS and NERSC's "superfacility," enabling 3D micron-resolution imaging for diverse scientific applications, from earth science to biology, with advanced data processing workflows.

• YouTube
• 37 minutes
• On-Demand
• Free Video

Machine Learning for Atomic-Scale Modeling - Potentials and Beyond - IPAM at UCLA

Explore machine learning techniques for atomic-scale modeling, addressing challenges in chemical diversity and integrating functional properties beyond interatomic potentials for advanced materials simulation.

• YouTube
• 51 minutes
• On-Demand
• Free Video

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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• Free Video