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Scientific Machine Learning (SciML)
00: Perceptron
01: Classification
02: Physics-Informed Neural Networks (PINNs)
03: Ordinary Differential Equations in SciML
04: Partial Differential Equation and Finite Difference
05: PINNs and steady-state heat transfer
06: Forward and inverse modeling of Burger’s Equation
07: Automatic Differentiation
08: DeepONet
08a: Gaussian Process
09: Physics-Informed DeepONet
10: Scale-Invariance and Inversion
10a: Simple Example comparing Different Optimizers
11. Bayesian regression with linear basis function models
Probability Distributions
11b: Bayesian Neural Networks
12: Graph Neural Network
13: Sparse Identification of Nonlinear Dynamical systems (SINDy)
13a: Discovering equation from experimental data using SINDy
14: Normalizing Flows
14a: Variational Inference with Normalizing Flows
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