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Summary Many 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.
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The Many Facets of Data Equity
Data-driven systems can be unfair, in many different ways. All too often, as data scientists, we focus narrowly on one technical aspect of fairness. In this paper, we attempt to address equity broadly, and identify the many different ways in which it is manifest in data-driven systems.
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- Award ID(s):
- 1934464
- PAR ID:
- 10287321
- Editor(s):
- Costa, Constantinos; Pitoura, Evaggelia
- Date Published:
- Journal Name:
- theWorkshop Proceedings of the EDBT/ICDT 2021 Joint Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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