NUCLEUS-MoE is a single neural network architecture that models saturated and subcooled pool boiling for dielectrics, refrigerants, and cryogens with generalization to new fluids.
Mitigating spectral bias in neural operators via high-frequency scaling for physical systems.arXiv preprint arXiv:2503.13695
3 Pith papers cite this work. Polarity classification is still indexing.
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An approximate greedy router for hybrid PDE solvers that mimics optimal selection without true error access and shows faster, more stable error reduction on test equations.
MENO restores multi-scale structure in neural-operator PDE surrogates via one-step improved MeanFlow, claiming up to 2× better power-spectrum accuracy and up to 14× faster inference than DDIM enhancement.
citing papers explorer
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NUCLEUS-MoE: Unified Model of Pool Boiling for Liquid Cooling
NUCLEUS-MoE is a single neural network architecture that models saturated and subcooled pool boiling for dielectrics, refrigerants, and cryogens with generalization to new fluids.
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A Greedy PDE Router for Blending Neural Operators and Classical Methods
An approximate greedy router for hybrid PDE solvers that mimics optimal selection without true error access and shows faster, more stable error reduction on test equations.
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MENO: MeanFlow-Enhanced Neural Operators for Dynamical Systems
MENO restores multi-scale structure in neural-operator PDE surrogates via one-step improved MeanFlow, claiming up to 2× better power-spectrum accuracy and up to 14× faster inference than DDIM enhancement.