pysindy
Stochastic-Processes
pysindy | Stochastic-Processes | |
---|---|---|
5 | 1 | |
1,293 | 30 | |
2.8% | - | |
9.3 | 6.4 | |
9 days ago | 12 months ago | |
Python | Python | |
GNU General Public License v3.0 or later | - |
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pysindy
- Sam Altman's ouster was precipitated by letter to board about AI breakthrough
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Ask HN: Why don't datacenters have passive rooflines like Net Zero homes?
https://github.com/dynamicslab/pysindy/issues/383#event-1002...
awesome-machine-learning-fluid-mechanics >
- Why are neural networks not multi-dimensional?
- Creating an equation for a system given its data.
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Physics-Based Deep Learning Book
Not MLP, but PySindy[0] might be a way to find a closed form solution. A simple MLP may not be best way to achieve what are you are after.
[0] https://github.com/dynamicslab/pysindy
Stochastic-Processes
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New Book: Gentle Introduction To Chaotic Dynamical Systems
Authored by Dr. Vincent Granville, 82 pages, published in March 2023. Available on our e-Store exclusively, here. See the table contents or sample chapter on GitHub here. The Python code is also in the same repository.
What are some alternatives?
sysidentpy - A Python Package For System Identification Using NARMAX Models
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
torchsde - Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
GPflow - Gaussian processes in TensorFlow
Poincare-Maps - MATLAB files for discovery of Poincaré maps
NeuralCDE - Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)
thebe - Turn static HTML pages into live documents with Jupyter kernels.
dynamo-release - Inclusive model of expression dynamics with conventional or metabolic labeling based scRNA-seq / multiomics, vector field reconstruction and differential geometry analyses
pbdl-book - Welcome to the Physics-based Deep Learning Book (v0.2)
Point-Processes - This repository contains the material (datasets, code, videos, spreadsheets) related to my book Stochastic Processes and Simulations - A Machine Learning Perspective.