Theory-guided hard constraint projection (HCP): a knowledge-based data-driven scientific machine learning method
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Publication:2133056
DOI10.1016/j.jcp.2021.110624OpenAlexW3112778626MaRDI QIDQ2133056
Yuntian Chen, Dou Huang, Jinyue Yan, Haoran Zhang, Nanzhe Wang, Dongxiao Zhang, Junsheng Zeng
Publication date: 29 April 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2012.06148
Artificial intelligence (68Txx) Probabilistic methods, stochastic differential equations (65Cxx) Mathematical biology in general (92Bxx)
Related Items (5)
Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios ⋮ HRW: Hybrid Residual and Weak Form Loss for Solving Elliptic Interface Problems with Neural Network ⋮ Elasticity-mechanics-informed generative adversarial networks for predicting the thermal strain of thermal barrier coatings penetrated by CaO-MgO-\(\mathrm{Al_2O}_3\)-\(\mathrm{SiO}_2\) ⋮ A new computationally simple approach for implementing neural networks with output hard constraints ⋮ A peridynamic-informed neural network for continuum elastic displacement characterization
Uses Software
Cites Work
- Unnamed Item
- On the design of an expert system guide for the use of scientific software
- Neocognition: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
- Neural network modeling for near wall turbulent flow.
- B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
- Adversarial uncertainty quantification in physics-informed neural networks
- Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
- A composite neural network that learns from multi-fidelity data: application to function approximation and inverse PDE problems
- Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Knowledge engineering: Principles and methods
- A projection neural network and its application to constrained optimization problems
- Neural networks and physical systems with emergent collective computational abilities.
- Application of the Karhunen-Loève Expansion to Feature Selection and Ordering
- Deep Blue
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