Operator learning for predicting multiscale bubble growth dynamics-Reference-Cited by-同舟云学术

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Operator learning for predicting multiscale bubble growth dynamics

Published:2021-03-14 Issue:10 Volume:154 Page:104118
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Lin Chensen¹^ORCID,Li Zhen²^ORCID,Lu Lu³^ORCID,Cai Shengze¹,Maxey Martin¹^ORCID,Karniadakis George Em¹

Affiliation:

1. Division of Applied Mathematics, Brown University, Providence, Rhode Island 02912, USA

2. Department of Mechanical Engineering, Clemson University, Clemson, South Carolina 29634, USA

3. Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

Funder

U.S. Department of Energy

ARL-Utah

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

https://aip.scitation.org/doi/am-pdf/10.1063/5.0041203

Reference47 articles.

1. Data-driven discovery of partial differential equations

2. Data-driven discovery of coordinates and governing equations

3. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

4. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

5. SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems

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