Physics Informed Machine Learning Courses and Certifications

Steve Brunton

Physics Informed Machine Learning - Embedding Physics in AI and Discovering New Physics with ML

Discover how to enhance machine learning by embedding physics principles, creating interpretable models that incorporate symmetries, conservation laws, and sparse dynamics.

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

Steve Brunton

Fourier Neural Operator - Physics-Informed Machine Learning

Explore Fourier Neural Operators for physics-informed machine learning, covering zero-shot super resolution, mesh invariance, and applications in Green's functions and Laplace operators.

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

Steve Brunton

Deep Operator Networks (DeepONet) - Physics Informed Machine Learning

Explore DeepONet's innovative approach to physics-informed machine learning, its solution operator concept, and practical applications in scientific computing.

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

Steve Brunton

Physics Informed Machine Learning: Recap and Summary

Comprehensive overview of Physics Informed Machine Learning, exploring key concepts, architectures, and applications in engineering, with emphasis on integrating physics into various stages of machine learning.

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

GERAD Research Center

Physics-Informed Machine Learning for Complex Systems - A Focus on Power System Applications

Explorez l'application de l'apprentissage automatique aux systèmes électriques, en mettant l'accent sur les modèles réduits et la calibration à partir de données enregistrées pour améliorer la résilience du réseau.

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

Inside Livermore Lab

Physics Informed Machine Learning through Symbolic Regression

Explore symbolic regression for identifying PDE models from scarce, noisy data. Compare with Physics-Informed Neural Networks and discuss applications in fault detection and system health prognostics.

• YouTube
• 1 hour 9 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Physics and Uncertainty in the Community Research Earth Digital Intelligence Twin

Explore AI-driven Earth system modeling with physics constraints and uncertainty quantification for improved climate predictions and ensemble generation.

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

Steve Brunton

Physics Informed Machine Learning: High-Level Overview of AI and ML in Science and Engineering

1 review

Explore how to integrate physics into machine learning, enhancing model accuracy and efficiency in engineering applications. Learn to leverage prior physical knowledge across all stages of the ML process.

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

Steve Brunton

Deep Learning to Discover Coordinates for Dynamics - Autoencoders & Physics Informed Machine Learning

Explore deep learning techniques for discovering effective coordinate systems in dynamical systems, enabling simpler models and physical law discovery. Focus on autoencoders and physics-informed machine learning.

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

Steve Brunton

Python Symbolic Regression - Physics Informed Machine Learning

Explore Python Symbolic Regression for physics-informed machine learning. Learn algorithm basics, historical context, and PySR tool benefits for discovering interpretable mathematical models from data.

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

Steve Brunton

Designing an Architecture for Physics-Informed Machine Learning - Part 3

Explore various neural network architectures for physics-informed machine learning, including ResNets, UNets, and PINNs. Learn how to incorporate physical principles and symmetries into model design for improved performance.

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

Steve Brunton

Neural Implicit Flow - Physics Informed Machine Learning

Explore physics-informed machine learning with Neural Implicit Flow, covering turbulent data compression, sparse sensor placement, and mesh-agnostic approaches for advanced fluid dynamics applications.

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

Alan Turing Institute

Uncertainty Quantification with Physics-Informed Machine Learning

Exploring uncertainty quantification in physics-informed machine learning, focusing on architecture and learning approaches for scientific applications and complex real-world phenomena.

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

Alan Turing Institute

Unique Challenges in Physics-Informed Machine Learning

Explores unique challenges in physics-informed machine learning, focusing on non-convexity in loss functions and insufficient model expressibility compared to conventional machine learning approaches.

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

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
• On-Demand
• Free Video