google-research/torchsde
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
Implements multiple SDE solver algorithms (Euler, Milstein, adaptive-step) with configurable noise types (scalar, diagonal, general) and supports both Itô and Stratonovich calculus. Enables end-to-end learning of latent SDEs and neural SDE-based generative models through PyTorch autograd, with adjoint-based sensitivity analysis for memory-efficient backpropagation. Integrates seamlessly with PyTorch's `nn.Module` API and demonstrated on applications including VAE-style latent dynamics and adversarial SDE training.
1,708 stars and 1,988,417 monthly downloads. Used by 8 other packages. No commits in the last 6 months. Available on PyPI.
Stars
1,708
Forks
223
Language
Python
License
Apache-2.0
Category
Last pushed
Dec 30, 2024
Monthly downloads
1,988,417
Commits (30d)
0
Dependencies
4
Reverse dependents
8
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