19 citations · 30 across the 3 of their papers we have counts for
3 papers
cs.CE2021★ 6 cited
Robust physics discovery via supervised and unsupervised pattern recognition using the Euler characteristic
Zhiming Zhang, Yongming Liu
Machine learning approaches have been widely used for discovering the underlying physics of dynamical systems from measured data. Existing approaches, however, still lack robustnes…
cs.LG2021★ 19 cited
Parsimony-Enhanced Sparse Bayesian Learning for Robust Discovery of Partial Differential Equations
Zhiming Zhang, Yongming Liu
Robust physics discovery is of great interest for many scientific and engineering fields. Inspired by the principle that a representative model is the one simplest possible, a new…
math.NA2021★ 5 cited
Robust Data-Driven Discovery of Partial Differential Equations under Uncertainties
Zhiming Zhang, Yongming Liu
Robust physics (e.g., governing equations and laws) discovery is of great interest for many engineering fields and explainable machine learning. A critical challenge compared with…