SDE
torchsde
| SDE | torchsde | |
|---|---|---|
| 1 | 5 | |
| 153 | 1,481 | |
| 0.0% | 2.0% | |
| 0.0 | 4.8 | |
| almost 3 years ago | 7 months ago | |
| MATLAB | Python | |
| MIT License | Apache License 2.0 |
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SDE
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Ask HN: Good Book on Stochastic Differential Equations?
I am not an expert in SDEs (my background is machine learning) but if I wanted to dig more into the subject, this is where I personally would start. Code [1] and pdf [2] for the book are available.
Simo Särkkä and Arno Solin (2019). Applied Stochastic Differential Equations. Cambridge University Press. Cambridge, UK.
[1] https://github.com/AaltoML/SDE
[2] https://users.aalto.fi/~asolin/sde-book/sde-book.pdf
torchsde
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Google Research • Differentiable SDE solvers with GPU support and efficient sensitivity analysis in PyTorch. For stochastic differential equations in your deep learning models
Github: https://github.com/google-research/torchsde
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[D] Ideal deep learning library
So not just that paper, but also our follow-up papers on the same topic: Neural SDEs as Infinite-Dimensional GANs Efficient and Accurate Gradients for Neural SDEs are in fact implemented in PyTorch, specifically the torchsde library. (Disclaimer: of which I am a developer.)
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[D] Is there any way for GAN to generate arbitrary length of time series signal?
Code: SDE-GAN example in torchsde.
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[P] Final Year Computer Science Project Suggestions
If you're interested in finance then I'd recommend Neural SDEs: https://arxiv.org/abs/2102.03657 https://arxiv.org/abs/2105.13493 https://github.com/google-research/torchsde/blob/master/examples/sde_gan.py
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Simple & Fast GAN Training [D]
This may or may not fit what you're after.
What are some alternatives?
score_sde - Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)
torchdyn - A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
DifferentialEquations.jl - Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
pysindy - A package for the sparse identification of nonlinear dynamical systems from data
gramm - Gramm is a complete data visualization toolbox for Matlab. It provides an easy to use and high-level interface to produce publication-quality plots of complex data with varied statistical visualizations. Gramm is inspired by R's ggplot2 library.
tabnet - PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf
Financial-Models-Numerical-Methods - Collection of notebooks about quantitative finance, with interactive python code.
SSD-pytorch - SSD: Single Shot MultiBox Detector pytorch implementation focusing on simplicity
ModelingToolkit.jl - An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
NeuralCDE - Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)
functorch - functorch is JAX-like composable function transforms for PyTorch.
pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs