Scientific Machine Learning Courses and Certifications

The Julia Programming Language

Scientific Machine Learning: Opportunities and Challenges - Keynote

Explore Scientific Machine Learning: combining physics-based models with ML for complex phenomena, addressing challenges in computational science, and revolutionizing fields like rocket engine combustion analysis.

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

Scientific Machine Learning - Where Physics-based Modeling Meets Data-driven Learning

Explore the synergy of physics-based modeling and data-driven learning in scientific machine learning, addressing complex challenges in science, engineering, and medicine with interpretable and scalable solutions.

• YouTube
• 1 hour 13 minutes
• On-Demand
• Free Video

Inside Livermore Lab

Generalizing Scientific Machine Learning and Differentiable Simulation Beyond Continuous Models

Explore generalizing scientific machine learning beyond continuous models, covering differentiable simulation for chaotic systems, stochastic processes, and inverse problems in advanced scientific computing.

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

JuliaHub

Scientific Computing and Machine Learning for Industrial Modeling - An Introduction to SciML

Discover how combining scientific computing with machine learning accelerates simulators and enhances industrial modeling through differential equations and data-driven approaches.

• YouTube
• 1 hour 13 minutes
• On-Demand
• Free Video

The Julia Programming Language

Raising the Ki of a SciML Model - Enhancing GOKU-nets for Scientific Machine Learning

Explore advanced SciML techniques with GOKU-UI, enhancing Latent ODEs through attention mechanisms and multiple shooting. Learn to improve model performance and data efficiency for complex system modeling.

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

MICDE University of Michigan

Foundational Methods for Foundation Models in Scientific Machine Learning - Lecture 6

Explore foundational methods for scientific machine learning models, addressing challenges in integrating data-driven ML with domain-driven scientific computing methodologies.

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

Institute for Pure & Applied Mathematics (IPAM)

Sampling, Inference, and Data-Driven Physical Modeling in Scientific Machine Learning

Discover advanced sampling and inference techniques for data-driven physical modeling in scientific machine learning applications.

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

Inside Livermore Lab

Input-Space Scientific Machine Learning for PDE-Constrained Optimization of Geometries

Explore input-space scientific machine learning for PDE-constrained optimization, featuring Physics-Enhanced Deep Surrogates and HiLAB techniques for efficient geometric design.

• YouTube
• 1 hour 16 minutes
• On-Demand
• Free Video

The Julia Programming Language

Scientific Machine Learning Using Functional Mock-Up Units - JuliaCon 2024

Hands-on workshop integrating physical system knowledge into machine learning using Functional Mock-Up Units. Explore hybrid modeling with a robotic arm case study, combining simulation and neural networks.

• YouTube
• 3 hours 3 minutes
• On-Demand
• Free Video

Inside Livermore Lab

Infinite Dimensional Optimization for Scientific Machine Learning

Explore advanced optimization techniques in scientific machine learning, focusing on infinite-dimensional perspectives and the "first optimize, then discretize" paradigm for neural network applications.

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

Society for Industrial and Applied Mathematics

Scientific Machine Learning Enhanced Forecasting in Geological Carbon Storage

Discover how scientific machine learning enhances CO2 forecasting in geological carbon storage, combining physics-based models with ML to predict subsurface migration and trapping.

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

Inside Livermore Lab

Scientific Machine Learning - From Physics-Informed to Data-Driven

Explore scientific machine learning evolution from physics-informed neural networks to data-driven operator learning, featuring plasma turbulence applications in fusion devices.

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

Introduction to Scientific Machine Learning

1 review

Learn the basics of machine learning with hands-on practical examples on engineering applications.

• edX
• 16 weeks, 6-7 hours a week
• On-Demand
• Free Online Course (Audit)

Inside Livermore Lab

Neural Differentiable Physics: Unifying Numerical PDEs and Deep Learning for Data-Augmented Computational Physics

Explore Neural Differentiable Physics, unifying numerical PDEs and deep learning for enhanced computational modeling of complex physical systems. Learn innovative SciML approaches integrating physics principles with AI.

• YouTube
• 1 hour 28 minutes
• On-Demand
• Free Video

Inside Livermore Lab

Enhancing Data-Driven Workflows for Complex Simulations

Explore data-driven methods for complex simulations, focusing on Dynamic Mode Decomposition. Learn about data compression, in situ visualization, and streaming techniques to enhance engineering and scientific workflows.

• YouTube
• 1 hour 16 minutes
• On-Demand
• Free Video