pytorch implementation of sde solvers

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Quick description about pytorch implementation of sde solvers:

This library provides stochastic differential equation (SDE) solvers with GPU support and efficient backpropagation. examples/demo.ipynb gives a short guide on how to solve SDEs, including subtle points such as fixing the randomness in the solver and the choice of noise types. examples/latent_sde.py learns a latent stochastic differential equation, as in Section 5 of [1]. The example fits an SDE to data, whilst regularizing it to be like an Ornstein-Uhlenbeck prior process. The model can be loosely viewed as a variational autoencoder with its prior and approximate posterior being SDEs. The program outputs figures to the path specified by . Training should stabilize after 500 iterations with the default hyperparameters. examples/sde_gan.py learns an SDE as a GAN, as in [2], [3]. The example trains an SDE as the generator of a GAN, whilst using a neural CDE [4] as the discriminator.

Features:

• Requirements: Python >=3.6 and PyTorch >=1.6.0
• Neural SDEs as GANs
• Latent SDE
• GPU support and efficient backpropagation
• Stochastic differential equation (SDE) solvers
• Several keyword arguments are also accepted

Programming Language: Python.
Categories:

Machine Learning

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