DiffEqML/torchdyn

Health v2 · maintenance only

Adoption not part of the score

1578 stars · 135 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Torchdyn is a PyTorch library dedicated to numerical deep learning, providing tools for neural differential equations, implicit models, and related numerical methods. It offers classes like NeuralODE and NeuralSDE, a functional API for GPU-compatible numerical solvers, and extensive tutorials.

Use cases

• train neural ODEs in PyTorch
• build continuous-depth neural network models
• implement neural SDEs for stochastic dynamics
• model dynamical systems with deep learning
• solve implicit deep equilibrium models
• apply numerical methods to deep learning research
• benchmark neural differential equation solvers

When to choose

• you need neural differential equations integrated with PyTorch
• you want GPU-compatible numerical solvers with a functional API
• you are researching continuous-depth models, neural ODEs, or deep equilibrium models
• you want tutorials and benchmarks for numerical deep learning

When to avoid

• you need a framework-agnostic solution outside PyTorch
• you only need classical ODE solvers without deep learning integration
• you require production-hardened, long-term-stable APIs for critical systems

Facets

library · maturity active

machine-learning deep-learning simulation math deep-learning machine-learning simulation python cross-platform neural-ode neural-differential-equations pytorch dynamical-systems deep-equilibrium-models numerical-methods implicit-models control-theory algorithms gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="DiffEqML/torchdyn")