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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 GalacticOptim.jl
    • Neural Ordinary Differential Equations with Flux.train!
    • GPU-based MNIST Neural ODE Classifier
    • 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
  • Training Techniques
    • Multiple Shooting
    • Strategies to Avoid Local Minima
    • 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
  • Delay Differential Equation (DDE) Tutorials
    • Delay Differential Equations
  • Differential-Algebraic Equation (DAE) Tutorials
    • 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 Diffrential 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
    • GalacticOptim.jl
  • Benchmarks
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  • Universal Differential Equations and Physical Layer Tutorials
  • Universal Ordinary, Stochastic, and Partial Diffrential Equation Examples
  • Universal Ordinary, Stochastic, and Partial Diffrential Equation Examples
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Universal Ordinary, Stochastic, and Partial Diffrential Equation Examples

For examples of using universal ordinary and stochastic differential equations, along with universal partial differential equations, see the Universal Differential Equations for Scientific Machine Learning paper along with its examples repository

« Controlling Stochastic Differential EquationsPhysics Informed Machine Learning with TensorLayer »

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