Uncertainty Quantification Courses and Certifications

Hausdorff Center for Mathematics

Uncertainty Quantification for Kinetic Equations I

Overview of uncertainty quantification for kinetic equations, covering Monte Carlo methods, multi-fidelity techniques, and stochastic Galerkin particle methods. Explores computational challenges and recent advancements in the field.

• YouTube
• 1 hour 4 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Uncertainty Quantification of Quantum Chemical Methods - IPAM at UCLA

Explore uncertainty quantification in quantum chemistry, covering error estimation, benchmarking, and machine learning approaches for improved accuracy in computational methods.

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

Institute for Pure & Applied Mathematics (IPAM)

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

Institute for Pure & Applied Mathematics (IPAM)

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

Institute for Pure & Applied Mathematics (IPAM)

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

Hausdorff Center for Mathematics

Uncertainty Quantification for Kinetic Equations II

Explore uncertainty quantification for kinetic equations, focusing on Monte Carlo methods, multi-fidelity approaches, and stochastic Galerkin particle techniques to address high-dimensional challenges.

• YouTube
• 1 hour 3 minutes
• On-Demand
• Free Video

Alan Turing Institute

Efficient and Effective Calibration of Spatio-Temporal Models - Dan Williamson & James Salter, Exeter

Explore efficient calibration techniques for spatio-temporal models, focusing on uncertainty quantification and Gaussian Process emulation in complex physical systems like climate and natural disasters.

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

Alan Turing Institute

Deep and Multi-Fidelity Learning with Gaussian Processes

Explore uncertainty quantification using Gaussian Process emulators for complex systems, focusing on deep learning and multi-fidelity approaches in climate, tsunami, and earthquake modeling.

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

Alan Turing Institute

High-Dimensional Hierarchical Models for Large-Scale Geophysical Applications

Explore high-dimensional hierarchical models for geophysical applications, focusing on uncertainty quantification and Gaussian Process emulation in complex physical systems.

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

Alan Turing Institute

Sequential Design Based on Mutual Information for Computer Experiments - Joakim Beck, KAUST

Explore uncertainty quantification using Gaussian Process emulators for complex systems. Learn about sequential design methods, mutual information criteria, and applications in climate, tsunami, and earthquake modeling.

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

Alan Turing Institute

Known Boundary Emulation of Complex Computer Models - Ian Vernon, Cambridge

Explore efficient methods for emulating complex computer models using Known Boundary Emulation, focusing on uncertainty quantification and applications in climate, tsunami, and earthquake problems.

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

Alan Turing Institute

Deep Gaussian Processes for Bayesian Inversion - Matt Dunlop, Courant

Explore deep Gaussian processes for Bayesian inversion, covering theoretical foundations, numerical methods, and applications in uncertainty quantification for complex physical systems.

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

Alan Turing Institute

Pragmatically Ambitious Multiscale Global Temperature Reconstruction - Finn Lindgren, Edinburgh

Explore pragmatic approaches to global temperature reconstruction using multiscale models, data sources, and statistical techniques for uncertainty quantification and bias correction.

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

Seds Online

The Carbonate Forum - The Importance of Quantification in Carbonate Sedimentology

Explore quantitative approaches in carbonate sedimentology, discussing uncertainty, numerical models, and interdisciplinary collaboration for improved geological understanding and interpretation.

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

Alan Turing Institute

Workshop on Probabilistic Numerical Methods - Panel Discussion

Panel discussion exploring probabilistic numerical methods, covering topics like reference priors, robust uncertainty quantification, and Bayesian inversion methods for improved statistical inference and error quantification.

• YouTube
• 1 hour 7 minutes
• On-Demand
• Free Video