Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics-Reference-Cited by-同舟云学术

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Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics

Published:2021-08-31 Issue:36 Volume:125 Page:8098-8106
ISSN:1089-5639
Container-title:The Journal of Physical Chemistry A
language:en
Short-container-title:J. Phys. Chem. A

Author:

Ji Weiqi¹^ORCID,Qiu Weilun²,Shi Zhiyu²,Pan Shaowu³,Deng Sili¹^ORCID

Affiliation:

1. Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

2. College of Engineering, Peking University, Beijing 100871, China

3. Department of Aerospace Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States

Funder

Massachusetts Institute of Technology

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpca.1c05102

Reference49 articles.

1. Data driven governing equations approximation using deep neural networks

2. Deep learning of vortex-induced vibrations

3. Graph Neural Networks for Prediction of Fuel Ignition Quality

4. Learning data-driven discretizations for partial differential equations

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1. Higher-order multi-scale physics-informed neural network (HOMS-PINN) method and its convergence analysis for solving elastic problems of authentic composite materials;Journal of Computational and Applied Mathematics;2025-03

2. A Python tool for parameter estimation of “black box” macro- and micro-kinetic models with Bayesian optimization – petBOA;Computer Physics Communications;2025-01

3. Modeling and optimization for the continuous catalytic reforming process based on the hybrid surrogate optimization model;Computers & Chemical Engineering;2024-12

4. CRK-PINN: A physics-informed neural network for solving combustion reaction kinetics ordinary differential equations;Combustion and Flame;2024-11

5. Physics-informed and data-driven modeling of an industrial wastewater treatment plant with actual validation;Computers & Chemical Engineering;2024-10

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