# Sparsistent Model Discovery

@article{Tod2021SparsistentMD, title={Sparsistent Model Discovery}, author={Georges Tod and Gert-Jan Both and Remy Kusters}, journal={ArXiv}, year={2021}, volume={abs/2106.11936} }

Discovering the partial differential equations underlying a spatio-temporal datasets from very limited observations is of paramount interest in many scientific fields. However, it remains an open question to know when model discovery algorithms based on sparse regression can actually recover the underlying physical processes. We trace back the poor of performance of Lasso based model discovery algorithms to its potential variable selection inconsistency: meaning that even if the true model is…

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Discovering PDEs from Multiple Experiments

- Computer Science, MathematicsArXiv
- 2021

A randomised adaptive group Lasso sparsity estimator is introduced to promote grouped sparsity and implement it in a deep learning based PDE discovery framework to create a learning bias that implies the a priori assumption that all experiments can be explained by the same underlying PDE terms with potentially different coefficients.

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