The MedEQ Library’s Documentation#

Autonomously driving equation discovery, from the micro to the macro, from laptops to supercomputers.

It builds on the fantastic PySR (MilesCranmer/PySR) and fvGP (lbl-camera/fvGP) libraries to create a user-facing package offering:

  • Discovery of symbolic closed-form equations that model multiple responses.

  • Efficient parameter sampling for planning experimental / simulational campaigns.

  • System multi-response uncertainty quantification - and specifically targeting high-variance parameter regions.

  • Automatic parallelisation of complex user simulation scripts on OS Processes and distributed supercomputers.

  • Interactive plotting of responses, uncertainties, discovered model outputs.

  • Language-agnostic saving of results found.

MedEQ was developed to discover physical laws and correlations in chemical engineering, but it is data-agnostic - and works with both simulated and experimental results in any domain.

Tutorials and Documentation#

At the top of this page, see the “Getting Started” tab for installation help; the “Tutorials” section has some explained high-level examples of the library. Finally, all exported functions are documented in the “Manual”.

Contributing#

You are more than welcome to contribute to this library in the form of library improvements, documentation or helpful examples; please submit them either as:

Citing#

If you use this library in your research, you are kindly asked to cite:

<Paper after publication>

This library would not have been possible without the excellent PySR library (MilesCranmer/PySR) which forms the core of the equation discovery subroutine; if you use MedEQ in your work, please also cite:

Cranmer M, Sanchez Gonzalez A, Battaglia P, Xu R, Cranmer K, Spergel D, Ho S. Discovering symbolic models from deep learning with inductive biases. Advances in Neural Information Processing Systems. 2020;33:17429-42.

Licensing#

MedEQ is licensed under the GPL v3.0 license.

Indices and tables#

Documentation

Pages