Physics Informed Machine Learning Courses and Certifications

ADC - Audio Developer Conference

Pipeline for VA Modelling with Physics-Informed Machine Learning

Explore a VA modelling pipeline using Differentiable Wave Digital Filters and neural networks for accurate, real-time circuit modelling, demonstrated with a diode clipper circuit.

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

MathWorks

AI for Engineering: An Overview

Explore how AI enhances engineering workflows—from simulation and virtual sensors to predictive maintenance and digital twins—while applying responsible AI principles like explainability and observability.

• Coursera
• 1 hour 11 minutes
• Self-Paced
• Paid Course

Computational Data Science in Physics III

Dive into numerical simulations for physics research, from differential equations to lattice models, culminating in applications relevant to contemporary lattice QCD.

• MITx Online
• 7 weeks, 10-15 hours/week
• Self-Paced
• Free Online Course

Steve Brunton

AI/ML and Physics - Choosing What to Model in Physics-Informed Machine Learning

Explore the intersection of AI/ML and physics, focusing on problem formulation and incorporating physics into machine learning models. Learn through case studies and practical applications.

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

Steve Brunton

Physics-Informed Machine Learning: Curating Training Data - Part 2

Explore physics-informed data curation for machine learning, covering data augmentation, coordinate systems, simulation vs. experiments, and strategies for handling expensive, biased, and rare data in AI/ML applications.

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

Alan Turing Institute

Physics-informed Machine Learning for Robust Pedestrian Detection in Embedded Low-power Systems

Innovative machine learning algorithm for efficient pedestrian detection in urban areas using neuromorphic sensors, combining physical motion models for accurate real-time tracking day and night.

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

EuroPython Conference

Physics-Informed Machine Learning - Fusing Scientific Laws with Machine Learning

Discover how Physics-Informed Neural Networks combine scientific laws with ML to solve complex simulations more efficiently than traditional methods, using Python libraries like PyTorch.

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

Steve Brunton

Physics Informed Neural Networks (PINNs) - Introduction and Applications

1 rating

Introduction to Physics Informed Neural Networks (PINNs): combining neural networks with physical laws to solve complex problems in fluid dynamics and beyond. Explores advantages, applications, and extensions of this innovative approach.

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

Steve Brunton

Neural ODEs - From ResNet to Continuous-Time Machine Learning

Explore Neural ODEs: a powerful machine learning approach for learning ODEs from data. Discover its advantages over ResNet and extensions like HNNs and LNNs.

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

Alan Turing Institute

Machine Learning in Fluid Dynamics and Climate Physics

Explore machine learning applications in fluid dynamics and climate modeling, including physics-informed approaches, data integration, and simulation validation techniques.

• YouTube
• 1 hour 2 minutes
• Self-Paced
• Free Video

Inside Livermore Lab

Physics-Enhanced Gaussian Processes for Learning of Electromechanical Systems

Explore physics-enhanced Gaussian processes for modeling electromechanical systems. Learn about GP-PHS and physics-enhanced variational autoencoders to improve data efficiency and ensure physical correctness.

• YouTube
• 1 hour 15 minutes
• Self-Paced
• Free Video

Steve Brunton

Crafting a Physics-Informed Loss Function for AI/ML in Physics - Part 4

Explores designing physics-informed loss functions in AI/ML, including case studies on fluid dynamics, sparse modeling, and equivariant loss, to enhance model performance and incorporate physical constraints.

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

DataLearning@ICL

Physics-Informed Deep Learning - Learning from Small Data

Explore physics-informed deep learning techniques for extracting insights from limited datasets, enhancing model accuracy and efficiency in scientific applications.

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