Coarse-scale PDEs from fine-scale observations via machine learning-Reference-Cited by-同舟云学术

Coarse-scale PDEs from fine-scale observations via machine learning

Published:2020-01 Issue:1 Volume:30 Page:013141
ISSN:1054-1500
Container-title:Chaos: An Interdisciplinary Journal of Nonlinear Science
language:en
Short-container-title:Chaos

Author:

Lee Seungjoon¹^ORCID,Kooshkbaghi Mahdi²,Spiliotis Konstantinos³,Siettos Constantinos I.⁴,Kevrekidis Ioannis G.¹

Affiliation:

1. Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA

2. Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA

3. Institute of Mathematics, University of Rostock, 18051 Rostock, Germany

4. Dipartimento di Matematica e Applicazioni “Renato Caccioppoli,” Universitá degli Studi di Napoli Federico II, Corso Umberto I, 40, 80138 Napoli NA, Italy

Funder

Defense Advanced Research Projects Agency

National Institutes of Health

Publisher

AIP Publishing

Subject

Applied Mathematics,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics

Link

http://aip.scitation.org/doi/am-pdf/10.1063/1.5126869

Reference30 articles.

1. Perspective: Coarse-grained models for biomolecular systems

2. Nonlinear signal processing and system identification: applications to time series from electrochemical reactions

3. Model identification of a spatiotemporally varying catalytic reaction

4. A comparison of recurrent training algorithms for time series analysis and system identification

5. Identification of distributed parameter systems: A neural net based approach

Cited by 47 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Slow Invariant Manifolds of Singularly Perturbed Systems via Physics-Informed Machine Learning;SIAM Journal on Scientific Computing;2024-07-05

2. Task-oriented machine learning surrogates for tipping points of agent-based models;Nature Communications;2024-05-15

3. Data-driven model selections of second-order particle dynamics via integrating Gaussian processes with low-dimensional interacting structures;Physica D: Nonlinear Phenomena;2024-05

4. Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs;Journal of Fluid Mechanics;2024-04-16

5. Multi-Scale Simulation of Complex Systems: A Perspective of Integrating Knowledge and Data;ACM Computing Surveys;2024-04-03