Learning quantities of interest from parametric PDEs: An efficient neural-weighted Minimal Residual approach-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Learning quantities of interest from parametric PDEs: An efficient neural-weighted Minimal Residual approach

Published:2024-06 Issue: Volume:164 Page:139-149
ISSN:0898-1221
Container-title:Computers & Mathematics with Applications
language:en
Short-container-title:Computers & Mathematics with Applications

Author:

Brevis Ignacio^ORCID,Muga Ignacio^ORCID,Pardo David,Rodriguez Oscar,van der Zee Kristoffer G.

Funder

EU Framework Programme for Research and Innovation Marie Skłodowska-Curie Actions

National Agency for Research and Development

Ministry of Economic Affairs and Digital Transformation

Ministerio de Ciencia e Innovación

Engineering and Physical Sciences Research Council

Publisher

Elsevier BV

Reference29 articles.

1. Model reduction and neural networks for parametric PDEs;Bhattacharya;SMAI J. Comput. Math.,2021

2. A machine-learning minimal-residual (ML-MRes) framework for goal-oriented finite element discretizations;Brevis;Comput. Math. Appl.,2021

3. Neural control of discrete weak formulations: Galerkin, least-squares and minimal-residual methods with quasi-optimal weights;Brevis;Comput. Methods Appl. Mech. Eng.,2022

4. State estimation – the role of reduced models;Cohen,2022

5. Adaptivity and variational stabilization for convection-diffusion equations;Cohen;M2AN Math. Model. Numer. Anal.,2012

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