Variational system identification of the partial differential equations governing the physics of pattern-formation: Inference under varying fidelity and noise

Abstract

We present a contribution to the field of system identification of partial differential equations (PDEs), with emphasis on discerning between competing mathematical models of pattern-forming physics. The motivation comes from developmental biology, where pattern formation is central to the development of any multicellular organism, and from materials physics, where phase transitions similarly lead to microstructure. In both these fields there is a collection of nonlinear, parabolic PDEs that, over suitable parameter intervals and regimes of physics, can resolve the patterns or microstructures with comparable fidelity. This observation frames the question of which PDE best describes the data at hand. This question is particularly compelling because identification of the closest representation to the true PDE, while constrained by the functional spaces considered relative to the data at hand, immediately delivers insights to the physics underlying the systems. While building on recent work that uses stepwise regression, we present advances that leverage the variational framework and statistical tests. We also address the influences of variable fidelity and noise in the data.

Publication
Computer Methods in Applied Mechanics and Engineering
Date
Citation
Z. Wang, X. Huan, and K. Garikipati. Variational system identification of the partial differential equations governing the physics of pattern-formation: Inference under varying fidelity and noise. Computer Methods in Applied Mechanics and Engineering, Vol. 356, No. 1, pp. 44–74, 2019. https://dx.doi.org/10.1016/j.cma.2019.07.007

BibTeX

@article{Wang2019a,
author = {Wang, Zhenlin and Huan, Xun and Garikipati, Krishna},
doi = {10.1016/j.cma.2019.07.007},
journal = {Computer Methods in Applied Mechanics and Engineering},
number = {1},
pages = {44--74},
title = {{Variational system identification of the partial differential equations governing the physics of pattern-formation: Inference under varying fidelity and noise}},
volume = {356},
year = {2019}
}