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Sparse Nonlinear Dynamics Models with SINDy, Part 3: Effective Coordinates for Parsimonious Models
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Classroom Contents
Physics Informed Machine Learning - Embedding Physics in AI and Discovering New Physics with ML
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- 1 Physics Informed Machine Learning: High Level Overview of AI and ML in Science and Engineering
- 2 AI/ML+Physics Part 1: Choosing what to model [Physics Informed Machine Learning]
- 3 AI/ML+Physics Part 2: Curating Training Data [Physics Informed Machine Learning]
- 4 AI/ML+Physics Part 3: Designing an Architecture [Physics Informed Machine Learning]
- 5 AI/ML+Physics Part 4: Crafting a Loss Function [Physics Informed Machine Learning]
- 6 AI/ML+Physics Part 5: Employing an Optimization Algorithm [Physics Informed Machine Learning]
- 7 AI/ML+Physics: Recap and Summary [Physics Informed Machine Learning]
- 8 AI/ML+Physics: Preview of Upcoming Modules and Bootcamps [Physics Informed Machine Learning]
- 9 Deep Learning to Discover Coordinates for Dynamics: Autoencoders & Physics Informed Machine Learning
- 10 Sparse Identification of Nonlinear Dynamics (SINDy): Sparse Machine Learning Models 5 Years Later!
- 11 Sparse Nonlinear Dynamics Models with SINDy, Part 2: Training Data & Disambiguating Models
- 12 Sparse Nonlinear Dynamics Models with SINDy, Part 3: Effective Coordinates for Parsimonious Models
- 13 Sparse Nonlinear Dynamics Models with SINDy, Part 4: The Library of Candidate Nonlinearities
- 14 Sparse Nonlinear Dynamics Models with SINDy, Part 5: The Optimization Algorithms
- 15 Discrepancy Modeling with Physics Informed Machine Learning
- 16 Hamiltonian Neural Networks (HNN) [Physics Informed Machine Learning]
- 17 Lagrangian Neural Network (LNN) [Physics Informed Machine Learning]
- 18 Neural Implicit Flow (NIF) [Physics Informed Machine Learning]
- 19 Neural ODEs (NODEs) [Physics Informed Machine Learning]
- 20 Python Symbolic Regression (PySR) [Physics Informed Machine Learning]
- 21 Residual Networks (ResNet) [Physics Informed Machine Learning]
- 22 Fourier Neural Operator (FNO) [Physics Informed Machine Learning]
- 23 Deep Operator Networks (DeepONet) [Physics Informed Machine Learning]
- 24 Physics Informed Neural Networks (PINNs) [Physics Informed Machine Learning]
