Deep Operator Networks
PulseAugur coverage of Deep Operator Networks — every cluster mentioning Deep Operator Networks across labs, papers, and developer communities, ranked by signal.
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Deep Learning Models Tackle Complex Math Problems
Researchers have developed novel deep-learning methods, specifically Physics-Informed Neural Networks (PINNs) and Deep Operator Networks (DeepONets), to solve complex infinity and p-Laplace problems. These neural networ…
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Deep Operator Networks accelerate wave prediction models
Researchers have developed a Deep Operator Network (DeepONet) as a surrogate model to predict bulk wave parameters, aiming to reduce the computational cost of storm surge prediction. This surrogate model learns the unde…
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Deflation-PINNs framework identifies multiple PDE solutions using neural networks
Researchers have developed Deflation-PINNs, a novel framework that integrates physics-informed neural networks (PINNs) with Deep Operator Networks (DeepONets) to address the challenge of identifying multiple solutions f…
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New research quantifies theory-to-practice gap in neural networks and operators
Researchers have analyzed the sampling complexity for learning with ReLU neural networks and neural operators, deriving upper bounds on convergence rates based on the number of samples. This work establishes a unified t…
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Hybrid physics-informed neural networks advance electricity system design
A new review paper explores the use of hybrid physics-informed neural networks (PIML) for enhancing electricity systems. These methods embed physical laws into machine learning models, improving accuracy and efficiency,…