rtqichen/torchdiffeq

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6477 stars · 1000 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

torchdiffeq is a PyTorch library of differentiable ordinary differential equation (ODE) solvers, best known as the canonical implementation of neural ODEs. It exposes odeint and odeint_adjoint interfaces for solving initial value problems with gradients, using the adjoint method for O(1)-memory backpropagation and full GPU acceleration.

Use cases

• solve ODE initial value problems with gradients in PyTorch
• train neural ODE / continuous-depth models
• backpropagate through long ODE integrations without storing solver internals
• perform adjoint sensitivity analysis on dynamical systems
• run GPU-accelerated differentiable physics or trajectory simulation
• model systems with differentiable event-based termination

When to choose

• your model or loss involves integrating an ODE and you need end-to-end gradients in PyTorch
• integration horizons are long enough that standard backprop through solver internals exhausts memory
• you want adaptive-step (e.g., dopri5) ODE solvers that run on GPU as part of a deep learning pipeline
• you are reproducing or extending research on neural ODEs, continuous normalizing flows, or event-triggered dynamics

When to avoid

• you only need fast non-differentiable ODE solutions in NumPy/SciPy without deep learning
• your stack is JAX, TensorFlow, or another non-PyTorch framework
• you need production-grade stiff-solver features or DAEs beyond what torchdiffeq supports

Facets

library · maturity stable

math machine-learning simulation gpu-computing deep-learning machine-learning deep-learning mathematics simulation python cross-platform neural-odes ode-solver adjoint-method differentiable-programming numerical-integration dynamical-systems pytorch initial-value-problems scientific-computing continuous-depth-models gpu

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