Feb 1, 2024 · We propose a novel framework called resolution-invariant deep operator (RDO) that decouples the spatial domain of the input and output.
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Nov 20, 2024 · It can also resolve PDEs with complex geometries whereas FNO fails. Various numerical experiments demonstrate the advantage of our method over ...
This work proposes a novel framework called resolution-invariant deep operator (RDO) that decouples the spatial domain of the input and output and can also ...
Duration: 1:54:12
Posted: Apr 12, 2024
Posted: Apr 12, 2024
Presentation #2: Resolution invariant deep operator network for PDEs with complex geometries, Yue Qiu, College of Mathematics and Statistics of Chongqing ...
Resolution invariant deep operator network for PDEs with complex geometries ... Neural operators (NO) are discretization invariant deep learning methods with ...
Resolution invariant deep operator network for PDEs with complex geometries ... Operator for Learning Solution Operators of Partial Differential Equations.
This paper proposes a physics-guided data augmentation (PGDA) method to improve the accuracy and generalization of neural operator models.
We discuss the recent advancement in PDE learning, focusing on Physics Invariant Attention Neural Operator (PIANO). PIANO is a novel neural operator learni.
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[PDF] INO: Invariant Neural Operators for Learning Complex Physical ...
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We propose INO, a novel integral neural operator ar- chitecture that is translation- and rotation-invariant, to learn complex physical systems with guaranteed ...