{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"}],"enrichment":{"capability":"Solves stochastic differential equations (SDEs) with GPU support and efficient backpropagation through PyTorch, enabling gradient-based learning of SDE-based models.","skillfed_tags":["stochastic-processes","differentiable-computing","research-code"],"use_cases":["Training latent SDE models that fit stochastic processes to time-series data with learned drift and diffusion terms.","Building generative models using neural SDEs as the generator component in adversarial training frameworks.","Performing uncertainty quantification in neural networks by modeling outputs as solutions to SDEs.","Implementing stochastic adjoint sensitivity analysis for gradient-based optimization of SDE parameters.","Prototyping research on continuous-time stochastic processes in deep learning."],"what_it_does":"torchsde is a PyTorch library that solves stochastic differential equations with automatic differentiation and GPU acceleration. It implements SDE solvers that support backpropagation through the solution trajectory, enabling end-to-end learning of SDE-based generative and latent-variable models. The library handles both It\u00f4 and Stratonovich SDEs with configurable noise types and provides efficient adjoint sensitivity methods for gradient computation.\n\nThe package is built on torch, numpy, scipy, and trampoline, and is designed for researchers working with neural SDEs, latent SDE models, and SDE-based generative models like GANs. It abstracts away the numerical complexity of SDE solving while maintaining differentiability throughout the computation graph, making it suitable for probabilistic deep learning workflows.","worth_installing":"Yes, if you are actively researching neural SDEs or latent stochastic models and can work with a frozen codebase. The package is well-designed and has no known vulnerabilities, but it is archived and abandoned\u2014no future maintenance or updates should be expected. Use it for research prototyping or as a reference implementation, but be aware that you will be responsible for any fixes or adaptations needed for compatibility with newer PyTorch or Python versions."},"id":"torchsde","links":{"html":"https://skillfed.io/packages/torchsde","md":"https://skillfed.io/packages/torchsde.md","pypi":"https://pypi.org/project/torchsde/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2023-09-26","license_spdx":null,"license_treatment":"permissive","name":"torchsde","python_support":"supports_current","summary":"SDE solvers and stochastic adjoint sensitivity analysis in PyTorch."},"popularity":{"monthly_downloads":1827243,"position":3513,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.2.6"}