google-research/torchsde

Health v2 · maintenance only

Flags: archived

Adoption not part of the score

1726 stars · 230 forks observed · 2026-08-28

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

A PyTorch library providing differentiable stochastic differential equation (SDE) solvers with GPU support and efficient backpropagation via stochastic adjoint sensitivity analysis. It enables training neural SDEs and latent SDE models end-to-end with gradient-based optimization.

Use cases

• solve stochastic differential equations on GPU in PyTorch
• train neural SDEs with backpropagation
• fit latent SDE models to time series data
• compute gradients through SDE solvers efficiently
• generate stochastic time series with a GAN trained SDE generator
• simulate Brownian-motion-driven dynamical systems

When to choose

• you need differentiable SDE integration inside a PyTorch training loop
• you are building neural differential equation models with stochastic dynamics
• you need memory-efficient adjoint gradients for SDEs
• you want GPU-accelerated stochastic simulation of Ito or Stratonovich SDEs

When to avoid

• you need deterministic ODE solvers only (use torchdiffeq instead)
• you work outside PyTorch, e.g. in JAX or TensorFlow
• you need production-hardened numerical SDE tooling rather than a research library
• your project requires frequent updates or active feature development

Facets

library · maturity maintenance

machine-learning deep-learning simulation math deep-learning machine-learning simulation python cross-platform sde-solvers pytorch stochastic-differential-equations neural-differential-equations differentiable-programming adjoint-sensitivity brownian-motion algorithms gpu

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markdown · JSON · MCP: product_card(name="google-research/torchsde")