diffeqflux jl

We have hosted the application diffeqflux jl in order to run this application in our online workstations with Wine or directly.

Quick description about diffeqflux jl:

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

Programming Language: Julia.
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