Learning partial differential equations for biological transport models from noisy spatio-temporal data
Work
Year: 2020
Type: article
Abstract: We investigate methods for learning partial differential equation (PDE) models from spatio-temporal data under biologically realistic levels and forms of noise. Recent progress in learning PDEs from d... more
Institutions North Carolina State University, Statistical and Applied Mathematical Sciences Institute, University of California, Merced
Cites: 52
Cited by: 79
Related to: 10
FWCI: 5.581
Citation percentile (by year/subfield): 99.99
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
Funders Division of Mathematical Sciences, Division of Mathematical Sciences, Division of Integrative Organismal Systems
Grant IDS 1638521, 1514929, 1838314