Affiliation:
1. Department of Earth and Environmental Sciences Lawrence Berkeley National Laboratory Berkeley CA USA
2. Department of Statistics University of Missouri Columbia MO USA
3. Department of Statistics North Carolina State University Raleigh NC USA
Abstract
SummaryMany real‐world scientific processes are governed by complex non‐linear dynamic systems that can be represented by differential equations. Recently, there has been an increased interest in learning, or discovering, the forms of the equations driving these complex non‐linear dynamic systems using data‐driven approaches. In this paper, we review the current literature on data‐driven discovery for dynamic systems. We provide a categorisation to the different approaches for data‐driven discovery and a unified mathematical framework to show the relationship between the approaches. Importantly, we discuss the role of statistics in the data‐driven discovery field, describe a possible approach by which the problem can be cast in a statistical framework and provide avenues for future work.
Funder
National Science Foundation
U.S. Geological Survey
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
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