Frame Invariance and Scalability of Neural Operators for Partial Differential Equations
DOI10.4208/CICP.OA-2021-0256zbMath1495.65202arXiv2112.14769OpenAlexW4225884163WikidataQ114021242 ScholiaQ114021242MaRDI QIDQ5106293
Jiequn Han, Xu-Hui Zhou, Heng Xiao, Muhammad I. Zafar
Publication date: 16 September 2022
Published in: Communications in Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2112.14769
Artificial neural networks and deep learning (68T07) Applications to the sciences (65Z05) Complexity and performance of numerical algorithms (65Y20) Numerical solution of discretized equations for boundary value problems involving PDEs (65N22)
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- Reduced order methods for modeling and computational reduction
- Machine learning strategies for systems with invariance properties
- Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations. Application to transport and continuum mechanics.
- Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids
- Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems
- Data-driven modelling of the Reynolds stress tensor using random forests with invariance
- Learning nonlocal constitutive models with neural networks
- Data-driven operator inference for nonintrusive projection-based model reduction
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Frame Invariance and Scalability of Neural Operators for Partial Differential Equations
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