Physics-data combined machine learning for parametric reduced-order modelling of nonlinear dynamical systems in small-data regimes-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Physics-data combined machine learning for parametric reduced-order modelling of nonlinear dynamical systems in small-data regimes

Published:2023-02 Issue: Volume:404 Page:115771
ISSN:0045-7825
Container-title:Computer Methods in Applied Mechanics and Engineering
language:en
Short-container-title:Computer Methods in Applied Mechanics and Engineering

Author:

Fu Jinlong,Xiao Dunhui,Fu Rui,Li Chenfeng,Zhu Chuanhua,Arcucci Rossella,Navon Ionel M.

Funder

European Regional Development Fund

Engineering and Physical Sciences Research Council

Fundamental Research Funds for the Central Universities

Swansea University

Llywodraeth Cymru

Xi’an Jiaotong University

Publisher

Elsevier BV

Subject

Computer Science Applications,General Physics and Astronomy,Mechanical Engineering,Mechanics of Materials,Computational Mechanics

Reference53 articles.

1. A parameterized non-intrusive reduced order model and error analysis for general time-dependent nonlinear partial differential equations and its applications;Xiao;Comput. Methods Appl. Mech. Engrg.,2017

2. Non-intrusive reduced-order modeling for fluid problems: A brief review;Yu;Proc. Inst. Mech. Eng. G,2019

3. Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data;Sun;Comput. Methods Appl. Mech. Engrg.,2020

4. Physics-informed machine learning for reduced-order modeling of nonlinear problems;Chen;J. Comput. Phys.,2021

5. Resolution effect: An error correction model for intrinsic permeability of porous media estimated from lattice boltzmann method;Fu;Transp. Porous Media,2020

Cited by 22 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. PCDMD: Physics-constrained dynamic mode decomposition for accurate and robust forecasting of dynamical systems with imperfect data and physics;Computer Physics Communications;2024-11

2. Fast model calibration for predicting the response of breast cancer to chemotherapy using proper orthogonal decomposition;Journal of Computational Science;2024-10

3. ML-GLE: A machine learning enhanced Generalized Langevin equation framework for transient anomalous diffusion in polymer dynamics;Journal of Computational Physics;2024-10

4. Physics-informed boundary integral networks (PIBI-Nets): A data-driven approach for solving partial differential equations;Journal of Computational Science;2024-09

5. A data-driven computational framework for non-intrusive reduced-order modelling of turbulent flows passing around bridge piers;Ocean Engineering;2024-09

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号京ICP备18003416号-3