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Physics-informed Neural-Network Software for Molecular Dynamics
  Applications
v1v2v3 (latest)

Physics-informed Neural-Network Software for Molecular Dynamics Applications

6 November 2020
Taufeq Mohammed Razakh
Beibei Wang
Shane Jackson
R. Kalia
A. Nakano
K. Nomura
P. Vashishta
    PINN
ArXiv (abs)PDFHTML

Papers citing "Physics-informed Neural-Network Software for Molecular Dynamics Applications"

3 / 3 papers shown
Learning Biomolecular Motion: The Physics-Informed Machine Learning Paradigm
Learning Biomolecular Motion: The Physics-Informed Machine Learning Paradigm
Aaryesh Deshpande
AI4CE
446
1
0
10 Nov 2025
Mixed-Integer Optimisation of Graph Neural Networks for Computer-Aided
  Molecular Design
Mixed-Integer Optimisation of Graph Neural Networks for Computer-Aided Molecular DesignComputers and Chemical Engineering (Comput. Chem. Eng.), 2023
Tom McDonald
Calvin Tsay
Artur M. Schweidtmann
Neil Yorke-Smith
335
26
0
02 Dec 2023
Partial Differential Equations Meet Deep Neural Networks: A Survey
Partial Differential Equations Meet Deep Neural Networks: A SurveyIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022
Shudong Huang
Wentao Feng
Chenwei Tang
Jiancheng Lv
AI4CEAIMat
315
43
0
27 Oct 2022
1
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