Conformalized Quantum DeepONet Ensembles reduce operator inference from quadratic to linear complexity using QOrthoNNs and SPQCs while delivering distribution-free uncertainty guarantees through ensemble conformal prediction.
Journal of Computational Physics , volume =
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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.
MSAT, a multi-scale attention transformer, achieves state-of-the-art accuracy on complex-geometry PDE problems (L2_rel=0.0101 on Heat2D-CG) with 3.7x improvement over FNO and far lower inference time than Mamba-NO.
citing papers explorer
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Conformalized Quantum DeepONet Ensembles for Scalable Operator Learning with Distribution-Free Uncertainty
Conformalized Quantum DeepONet Ensembles reduce operator inference from quadratic to linear complexity using QOrthoNNs and SPQCs while delivering distribution-free uncertainty guarantees through ensemble conformal prediction.
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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
MSAT, a multi-scale attention transformer, achieves state-of-the-art accuracy on complex-geometry PDE problems (L2_rel=0.0101 on Heat2D-CG) with 3.7x improvement over FNO and far lower inference time than Mamba-NO.