Koopman autoencoders with forcings and temporal unrolling deliver accurate year-long predictions for coastal-ocean models at 300-1400x speedup, outperforming POD in two of three cases.
Rivera-Casillas, S
3 Pith papers cite this work. Polarity classification is still indexing.
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AMORE develops an adaptive multi-output DeepONet with custom losses, partition-of-unity trunk, and invertible/softmax mass-fraction maps to surrogate stiff kinetics on syngas (12 states) and GRI-Mech (24 states).
DeepONet surrogate model accurately predicts wave-induced radiation stress and wave heights in steady-state simulations as a replacement for the SWAN numerical model.
citing papers explorer
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Reduced-Order Surrogates for Forced Flexible Mesh Coastal-Ocean Models
Koopman autoencoders with forcings and temporal unrolling deliver accurate year-long predictions for coastal-ocean models at 300-1400x speedup, outperforming POD in two of three cases.
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AMORE: Adaptive Multi-Output Operator Network for Stiff Chemical Kinetics
AMORE develops an adaptive multi-output DeepONet with custom losses, partition-of-unity trunk, and invertible/softmax mass-fraction maps to surrogate stiff kinetics on syngas (12 states) and GRI-Mech (24 states).
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Operator Learning for Surrogate Modeling of Wave-Induced Forces from Sea Surface Waves
DeepONet surrogate model accurately predicts wave-induced radiation stress and wave heights in steady-state simulations as a replacement for the SWAN numerical model.