A quantum ensemble method reduces operator inference to linear complexity and supplies distribution-free uncertainty bounds for high-dimensional dynamical systems.
Journal of Computational Physics , volume =
3 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
ELGIN is a graph-based physics-informed surrogate model that predicts carrier flow and polydisperse particle motion in dental aerosol scenarios, achieving lower tracking errors and 37x speedup versus full OpenFOAM CFD in a preliminary single-case test.
The Multi-Scale Attention Transformer achieves state-of-the-art accuracy on PDEs with complex geometries, delivering 3.7 times lower error than FNO on a heat benchmark while running inference thousands of times faster than Mamba-NO.
citing papers explorer
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Conformalized Quantum DeepONet Ensembles for Scalable Operator Learning with Distribution-Free Uncertainty
A quantum ensemble method reduces operator inference to linear complexity and supplies distribution-free uncertainty bounds for high-dimensional dynamical systems.
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Physics-Informed Graph Neural Network Surrogates for Turbulent Nanoparticle Dispersion in Dental Clinical Environments
ELGIN is a graph-based physics-informed surrogate model that predicts carrier flow and polydisperse particle motion in dental aerosol scenarios, achieving lower tracking errors and 37x speedup versus full OpenFOAM CFD in a preliminary single-case test.
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When Attention Beats Fourier: Multi-Scale Transformers for PDE Solving on Irregular Domains
The Multi-Scale Attention Transformer achieves state-of-the-art accuracy on PDEs with complex geometries, delivering 3.7 times lower error than FNO on a heat benchmark while running inference thousands of times faster than Mamba-NO.