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DiffEqFlux.jl
  • DiffEqFlux.jl: Generalized Physics-Informed and Scientific Machine Learning (SciML)
  • Ordinary Differential Equation (ODE) Tutorials
    • Optimization of Ordinary Differential Equations
    • Parameter Estimation on Highly Stiff Systems
    • Neural Ordinary Differential Equations with sciml_train
    • GPU-based MNIST Neural ODE Classifier
    • Convolutional Neural ODE MNIST Classifier on GPU
    • Augmented Neural Ordinary Differential Equations
    • Smoothed Collocation for Fast Two-Stage Training
    • Neural Graph Differential Equations
    • Handling Exogenous Input Signals
    • Continuous Normalizing Flows with GalacticOptim.jl
  • Direct Usage with Optimizer Backends
    • Neural Ordinary Differential Equations with GalacticOptim.jl
    • Neural Ordinary Differential Equations with Flux.train!
  • Training Techniques
    • Multiple Shooting
    • Strategies to Avoid Local Minima
    • Prediction error method (PEM)
    • Handling Divergent and Unstable Trajectories
    • Simultaneous Fitting of Multiple Neural Networks
    • Data-Parallel Multithreaded, Distributed, and Multi-GPU Batching
    • Neural Second Order Ordinary Differential Equation
    • Newton and Hessian-Free Newton-Krylov with Second Order Adjoint Sensitivity Analysis
    • Training a Neural Ordinary Differential Equation with Mini-Batching
  • Stochastic Differential Equation (SDE) Tutorials
    • Optimization of Stochastic Differential Equations
    • Neural Stochastic Differential Equations
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    • Delay Differential Equations
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    • Enforcing Physical Constraints via Universal Differential-Algebraic Equations
  • Partial Differential Equation (PDE) Tutorials
    • Partial Differential Equation (PDE) Constrained Optimization
  • Hybrid and Jump Equation Tutorials
    • Training Neural Networks in Hybrid Differential Equations
    • Bouncing Ball Hybrid ODE Optimization
    • Neural Jump Diffusions (Neural Jump SDE) and Neural Partial Differential Equations (Neural PDEs)
  • Bayesian Estimation Tutorials
    • Bayesian Estimation of Differential Equations with Probabilistic Programming
    • Bayesian Neural ODEs: NUTS
    • Bayesian Neural ODEs: SGLD
  • Optimal and Model Predictive Control Tutorials
    • Solving Optimal Control Problems with Universal Differential Equations
    • Universal Differential Equations for Neural Feedback Control
    • Controlling Stochastic Differential Equations
  • Universal Differential Equations and Physical Layer Tutorials
    • Universal Ordinary, Stochastic, and Partial Differential Equation Examples
    • Physics Informed Machine Learning with TensorLayer
    • Hamiltonian Neural Network
  • Layer APIs
    • Classical Basis Layers
    • Tensor Product Layer
    • Continuous Normalizing Flows Layer
    • Spline Layer
    • Neural Differential Equation Layers
    • Hamiltonian Neural Network Layer
  • Manual and APIs
    • Controlling Choices of Adjoints
    • Use with Flux Chain and train!
    • FastChain
    • Smoothed Collocation
    • GPUs
    • sciml_train and GalacticOptim.jl
  • Benchmarks
Version
  • Hybrid and Jump Equation Tutorials
  • Neural Jump Diffusions (Neural Jump SDE) and Neural Partial Differential Equations (Neural PDEs)
  • Neural Jump Diffusions (Neural Jump SDE) and Neural Partial Differential Equations (Neural PDEs)
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Neural Jump Diffusions (Neural Jump SDE) and Neural Partial Differential Equations (Neural PDEs)

For the sake of not having a never-ending documentation of every single combination of CPU/GPU with every layer and every neural differential equation, we will end here. But you may want to consult this blog post which showcases defining neural jump diffusions and neural partial differential equations.

« Bouncing Ball Hybrid ODE OptimizationBayesian Estimation of Differential Equations with Probabilistic Programming »

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