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DiffEqFlux.jl is a Julia library that combines differential equations with neural networks, enabling the creation of neural differential equations (neural ODEs), universal differential equations, and physics-informed learning models. It serves as a bridge between the DifferentialEquations.jl and Flux.jl libraries, allowing for end-to-end differentiable simulations and model training in scientific machine learning. DiffEqFlux.jl is widely used for modeling dynamical systems with learnable components.
Features
- Combines neural networks and differential equations
- Integrates with Flux.jl and the broader SciML ecosystem
- Supports neural ODEs, SDEs, DDEs, and DAEs
- Enables gradient-based optimization of dynamic models
- Allows physics-informed machine learning and hybrid modeling
- Provides GPU and automatic differentiation support
Project Samples
Categories
Machine LearningLicense
MIT LicenseFollow DiffEqFlux.jl
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