pysindy
pbdl-book
| pysindy | pbdl-book | |
|---|---|---|
| 5 | 2 | |
| 1,293 | 832 | |
| 2.8% | 2.6% | |
| 9.3 | 5.0 | |
| 8 days ago | 3 days ago | |
| Python | Jupyter Notebook | |
| GNU General Public License v3.0 or later | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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
pbdl-book
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Using differential equations/dynamical systems in practical data science?
For anyone interested, I loved this resource: https://physicsbaseddeeplearning.org/
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Physics-Based Deep Learning Book
https://github.com/tum-pbs/pbdl-book/blob/main/_toc.yml and all the config is in one file: https://github.com/tum-pbs/pbdl-book/blob/main/_config.yml (the build system leverages Sphinx which is the docs workhorse in the Python world)
One of the coolest things is the "Launcher" option which gives readers the options to "run" any notebook interactively (using the rocket button in the top right). It's a one-line config https://github.com/tum-pbs/pbdl-book/blob/main/_config.yml#L... A similar config would enable the "Launch in Pybinder" option which is a free ephemeral jupyter provider, see https://mybinder.org/
This "execute anywhere" option is nicely abstracted away as the `thiebe` library, and there is even POC work to run a pyodide kernel (https://github.com/executablebooks/thebe/issues/465) so soon all of this goodness will work offline in your browser!
As an educator, it's hard not to get excited about the future, given the pace at which learning/teaching tooling is developing!
What are some alternatives?
sysidentpy - A Python Package For System Identification Using NARMAX Models
thebe - Turn static HTML pages into live documents with Jupyter kernels.
torchsde - Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
Poincare-Maps - MATLAB files for discovery of Poincaré maps
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
Stochastic-Processes - My book: Gentle Introduction to Chaotic Dynamical Systems. Includes stochastic dynamical systems and statistical properties of numeration systems in any dimension.
grade-school-math
awesome-machine-learning-fluid-mechanics - Curated list for ML in FM
awesome-machine-learning-fluid-m
awesome-fluid-dynamics - A curated list of repositories related to fluid dynamics.
open-sustainable-technology - A directory and analysis of the open source ecosystem in the areas of climate change, sustainable energy, biodiversity and natural resources.