google-research/torchsde
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
This tool helps researchers model systems that change randomly over time by solving stochastic differential equations (SDEs) efficiently. You provide the SDE definition, and it generates the numerical solutions and calculates how sensitive these solutions are to changes in the inputs. It's designed for machine learning researchers working on advanced modeling tasks like latent SDEs or generative adversarial networks.
1,708 stars. Used by 8 other packages. No commits in the last 6 months. Available on PyPI.
Use this if you are a machine learning researcher who needs to simulate complex systems with inherent randomness and requires accurate gradient computations for training deep learning models.
Not ideal if you are a data analyst or practitioner looking for off-the-shelf predictive models without delving into the underlying mathematical dynamics and model training.
Stars
1,708
Forks
223
Language
Python
License
Apache-2.0
Category
Last pushed
Dec 30, 2024
Commits (30d)
0
Dependencies
4
Reverse dependents
8
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