Scientific Machine Learning Courses and Certifications

Centre International de Rencontres Mathématiques

Linear and Nonlinear Schemes for Forward Model Reduction and Inverse Problems - Lecture 3

Explore linear and nonlinear schemes for forward model reduction and inverse problems in this advanced mathematics lecture by Olga Mula.

• YouTube
• 1 hour 11 minutes
• On-Demand
• Free Video

Centre International de Rencontres Mathématiques

Data-Driven Latent Representations for Time-Dependent Problems - Lecture 3

Explore data-driven latent representations for time-dependent problems, focusing on applications in climate downscaling, superresolution, and optimal transport.

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

Inside Livermore Lab

The Weak Form Is Stronger Than You Think - Data-Driven Physical Systems

Discover how weak form-based scientific machine learning revolutionizes data-driven modeling with enhanced accuracy, noise resistance, and computational efficiency in differential equations and model development.

• YouTube
• 1 hour 5 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Weak Form SciML for Learning Models on Different Scales

Discover weak form Scientific Machine Learning techniques for creating interpretable mathematical models across different scales from data without forward-solve discretizations.

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

The Julia Programming Language

JuliaSimBatteries.jl - Robust PDE Models of Lithium-ion Batteries

Discover advanced lithium-ion battery simulation techniques using Julia, enabling fast charging predictions, pack modeling, and lifetime analysis through sophisticated electrochemical and thermal physics modeling.

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

JuliaHub

Dyad SciML Tutorial - Model Discovery with Universal Differential Equations

Discover how to use Dyad's SciML capabilities to automatically identify missing physics in engineering models through Universal Differential Equations and neural networks.

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

Institute for Pure & Applied Mathematics (IPAM)

Structure-Preserving Machine Learning and Data-Driven Structure Discovery

Explore how to embed physical structures into machine learning models and discover conservation laws from data for improved scientific computing performance.

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

Institute for Pure & Applied Mathematics (IPAM)

System Identification via Invariant Measures

Discover system identification through invariant measures when trajectory data is sparse, featuring PDE-based approximation methods and optimal transportation techniques for dynamical systems.

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

Institute for Pure & Applied Mathematics (IPAM)

Regularized Wasserstein Proximal Algorithms for Nonsmooth Sampling Problems

Explore regularized Wasserstein proximal algorithms for tackling nonsmooth sampling problems in high-dimensional spaces with convergence guarantees.

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

JuliaHub

Building Digital Twins with JuliaSim - Scientific Machine Learning and Simulation

Discover how Scientific Machine Learning and equation-based modeling combine in JuliaSim to create powerful digital twins for next-generation simulation and modeling applications.

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

Alan Turing Institute

Leveraging Physics-Induced Bias in Scientific Machine Learning for Computational Mechanics

Explore physics-induced bias in scientific machine learning for computational mechanics, focusing on physics-informed, structure-preserved learning for irregular geometries.

• YouTube
• 1 hour 8 minutes
• On-Demand
• Free Video

Inside Livermore Lab

Scientific Machine Learning through the Lens of Physics-Informed Neural Networks

Explore physics-informed neural networks (PINNs) as an alternative to traditional scientific computation, addressing limitations and showcasing applications in solving complex physical problems.

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

VinAI

Model-Constrained Deep Learning Approaches for Scientific Machine Learning

Explore model-aware machine learning methods combining scientific computing and data-driven models. Learn about ROM-ML and Autoencoder-based Inversion approaches for PDE-constrained inverse problems.

• YouTube
• 1 hour 18 minutes
• On-Demand
• Free Video

Alan Turing Institute

Generalizing Scientific Machine Learning and Differentiable Simulation Beyond Continuous Models

Explore advanced scientific machine learning, extending differentiable simulation to complex models like jump diffusions and agent-based systems, addressing challenges in implementation and numerical stability.

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

Welcome to the interactive, multi-channel and collaborative course on Introduction to Scientific Programming and Machine Learning with Julia.

• Independent
• On-Demand
• Free Online Course