129 citations · 362 across the 5 of their papers we have counts for
5 papers
Learning fluid physics from highly turbulent data using sparse physics-informed discovery of empirical relations (SPIDER)
Daniel R. Gurevich, Matthew R. Golden, Patrick A. K. Reinbold +1
We show how a complete mathematical description of a complicated physical phenomenon can be learned from observational data via a hybrid approach combining three simple and general…
Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression
Patrick A. K. Reinbold, Logan M. Kageorge, Michael F. Schatz +1
Machine learning offers an intriguing alternative to first-principles analysis for discovering new physics from experimental data. However, to date, purely data-driven methods have…
Using Noisy or Incomplete Data to Discover Models of Spatiotemporal Dynamics
Patrick A. K. Reinbold, Daniel R. Gurevich, Roman O. Grigoriev
Sparse regression has recently emerged as an attractive approach for discovering models of spatiotemporally complex dynamics directly from data. In many instances, such models are…
Robust and optimal sparse regression for nonlinear PDE models
Daniel R. Gurevich, Patrick A. K. Reinbold, Roman O. Grigoriev
This paper investigates how models of spatiotemporal dynamics in the form of nonlinear partial differential equations can be identified directly from noisy data using a combination…
Data-driven discovery of partial differential equation models with latent variables
Patrick A. K. Reinbold, Roman O. Grigoriev
In spatially extended systems, it is common to find latent variables that are hard, or even impossible, to measure with acceptable precision, but are crucially important for the pr…