A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems-Reference-Cited by-同舟云学术

A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems

Published:2020-01 Issue: Volume:401 Page:109020
ISSN:0021-9991
Container-title:Journal of Computational Physics
language:en
Short-container-title:Journal of Computational Physics

Author:

Meng Xuhui,Karniadakis George Em

Funder

Air Force Office of Scientific Research

U.S. Department of Energy

Defense Advanced Research Projects Agency

Publisher

Elsevier BV

Subject

Computer Science Applications,Physics and Astronomy (miscellaneous),Applied Mathematics,Computational Mathematics,Modelling and Simulation,Numerical Analysis

Reference40 articles.

1. Approximation and model management in aerodynamic optimization with variable-fidelity models;Alexandrov;J. Aircr.,2001

2. An approach to multi-fidelity in conceptual aircraft design in distributed design environments;Böhnke,2011

3. A multi-fidelity modelling approach for evaluation and optimization of wing stroke aerodynamics in flapping flight;Zheng;J. Fluid Mech.,2013

4. Multidisciplinary unmanned combat air vehicle system design using multi-fidelity model;Nguyen;Aerosp. Sci. Technol.,2013

5. Identification of physical processes via combined data-driven and data-assimilation methods;Chang;J. Comput. Phys.,2019

Cited by 322 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. Gaussian process fusion method for multi-fidelity data with heterogeneity distribution in aerospace vehicle flight dynamics;Engineering Applications of Artificial Intelligence;2024-12

2. Ada2MF: Dual-adaptive multi-fidelity neural network approach and its application in wind turbine wake prediction;Engineering Applications of Artificial Intelligence;2024-11

3. Data-driven methods for the inverse problem of suspension system excited by jump and diffusion stochastic track excitation;International Journal of Non-Linear Mechanics;2024-11

4. Multi-fidelity deep neural network with Monte Carlo dropout technique for uncertainty-aware risk recognition of backward erosion piping in dikes;Applied Soft Computing;2024-11

5. Implicitly physics-informed multi-fidelity physical field data fusion method based on Taylor modal decomposition;Advanced Engineering Informatics;2024-10