Deep Operator Network
PulseAugur coverage of Deep Operator Network — every cluster mentioning Deep Operator Network across labs, papers, and developer communities, ranked by signal.
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Operator Learning Framework Enhances Power Grid Simulation Accuracy
Researchers have developed a novel Operator Learning framework to approximate the dynamic behavior of synchronous generators, a crucial component in power grids. This framework utilizes Deep Operator Networks (DeepONets…
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New Theory Explains Neural Scaling Laws in Operator Learning
This paper presents a theoretical framework for understanding neural scaling laws in deep operator networks, specifically focusing on architectures like DeepONet. The study analyzes approximation and generalization erro…
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Neural operators accelerate Bayesian inverse design in CFD by over 1000x
Researchers have developed a method to significantly speed up Bayesian inverse design for computational fluid dynamics (CFD) by integrating neural operators. This approach allows for the inference of aerodynamic geometr…
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New SON framework quantifies uncertainty in SPDEs
Researchers have developed a new framework called the Stochastic Operator Network (SON) for quantifying uncertainty in stochastic partial differential equations (SPDEs). This method combines Deep Operator Networks with …
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Neural networks struggle with extreme predictions in uncertainty propagation
A new study published on arXiv investigates the performance of neural network surrogate models in capturing the full distribution of solutions for stochastic problems, particularly focusing on the tails of the distribut…
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Deep Operator Networks predict composite material deformation with uncertainty quantification
Researchers have developed a Deep Operator Network (DeepONet) to predict process-induced deformation in carbon/epoxy composites. This data-driven surrogate model combines physics-based simulations with experimental meas…