Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
Por um escritor misterioso
Last updated 17 junho 2024
2108.06905] A physics-informed variational DeepONet for predicting the crack path in brittle materials - Academus scientific article reader
PDF) Arbitrary-Depth Universal Approximation Theorems for Operator Neural Networks
Brains, Minds + Machines Seminar Series: DeepOnet: Learning nonlinear operators based on the universal approximation theorem of operators
Introduction to Hybrid Modelling for Digital Twins
Operator Learning via Physics-Informed DeepONet: Let's Implement It From Scratch, by Shuai Guo
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators - University of Wales Trinity Saint David
Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms
A seamless multiscale operator neural network for inferring bubble dynamics, Journal of Fluid Mechanics
In-context operator learning with data prompts for differential equation problems
DeepOnet: Learning nonlinear operators based on the universal approximation theorem of operators.
NEW PROGRESS IN INTELLIGENT SOLUTION OF NEURAL OPERATORS AND PHYSICS-INFORMED-BASED METHODS
DeepONet: A deep neural network-based model to approximate linear and nonlinear operators
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
High‐Precision and Fast Prediction of Regional Wind Fields in Near Space Using Neural‐Network Approximation of Operators - Chen - 2023 - Geophysical Research Letters - Wiley Online Library
Learning Operators with Mesh-Informed Neural Networks
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