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2006.09535
Cited By
Multipole Graph Neural Operator for Parametric Partial Differential Equations
16 June 2020
Zong-Yi Li
Nikola B. Kovachki
Kamyar Azizzadenesheli
Burigede Liu
K. Bhattacharya
Andrew M. Stuart
Anima Anandkumar
AI4CE
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Papers citing
"Multipole Graph Neural Operator for Parametric Partial Differential Equations"
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Title
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Physics-Informed Deep Neural Operator Networks
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An extensible Benchmarking Graph-Mesh dataset for studying Steady-State Incompressible Navier-Stokes Equations
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Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning
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LordNet: An Efficient Neural Network for Learning to Solve Parametric Partial Differential Equations without Simulated Data
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Approximation of Functionals by Neural Network without Curse of Dimensionality
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Transformer for Partial Differential Equations' Operator Learning
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Variable-Input Deep Operator Networks
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Generic bounds on the approximation error for physics-informed (and) operator learning
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Nonparametric learning of kernels in nonlocal operators
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Applications of physics informed neural operators
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Can we integrate spatial verification methods into neural-network loss functions for atmospheric science?
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Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problems
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Message Passing Neural PDE Solvers
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Pseudo-Differential Neural Operator: Generalized Fourier Neural Operator for Learning Solution Operators of Partial Differential Equations
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Nonlocal Kernel Network (NKN): a Stable and Resolution-Independent Deep Neural Network
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Learning Operators with Coupled Attention
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Learned Coarse Models for Efficient Turbulence Simulation
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Frame invariance and scalability of neural operators for partial differential equations
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Learning High-Dimensional Parametric Maps via Reduced Basis Adaptive Residual Networks
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Factorized Fourier Neural Operators
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NeuralPDE: Modelling Dynamical Systems from Data
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Physics-Informed Neural Operator for Learning Partial Differential Equations
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Anima Anandkumar
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FC2T2: The Fast Continuous Convolutional Taylor Transform with Applications in Vision and Graphics
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Multiwavelet-based Operator Learning for Differential Equations
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Physics-informed Convolutional Neural Networks for Temperature Field Prediction of Heat Source Layout without Labeled Data
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