activity
20132018
most citedDiscovering governing equations from data: Sparse identification of nonlinear dynamical systems

4.8k citations · 6.6k across the 13 of their papers we have counts for

collaborators

16 papers

stat.AP2018

Sub-national levels and trends in contraceptive prevalence, unmet need, and demand for family planning in Nigeria with survey uncertainty

Laina D. Mercer, Fred Lu, Joshua L. Proctor

Ambitious global goals have been established to provide universal access to affordable modern contraceptive methods. The UN's sustainable development goal 3.7.1 proposes satisfying…

math.DS2018★ 75 cited

Model selection for hybrid dynamical systems via sparse regression

Niall M Mangan, Travis Askham, Steven L Brunton +2

Hybrid systems are traditionally difficult to identify and analyze using classical dynamical systems theory. Moreover, recently developed model identification methodologies largely…

eess.SY2017★ 2 cited

Dynamic mode decomposition for compressive system identification

Zhe Bai, Eurika Kaiser, Joshua L. Proctor +2

Dynamic mode decomposition has emerged as a leading technique to identify spatiotemporal coherent structures from high-dimensional data, benefiting from a strong connection to nonl…

q-bio.PE2017★ 9 cited

Identifying spatiotemporal dynamics of Ebola in Sierra Leone using virus genomes

Kyle B. Gustafson, Joshua L. Proctor

Containing the recent West African outbreak of Ebola virus (EBOV) required the deployment of substantial global resources. Operationally, health workers and surveillance teams trea…

physics.data-an2017★ 208 cited

Model selection for dynamical systems via sparse regression and information criteria

Niall M. Mangan, J. Nathan Kutz, Steven L. Brunton +1

We develop an algorithm for model selection which allows for the consideration of a combinatorially large number of candidate models governing a dynamical system. The innovation ci…

nlin.PS2016

Data-driven discovery of partial differential equations

Samuel H. Rudy, Steven L. Brunton, Joshua L. Proctor +1

We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The…