MeshTok uses AMR-inspired adaptive multiscale tokenization to improve the efficiency-accuracy trade-off of Transformer models for PDEs over uniform-grid baselines.
org/CorpusID:13905106
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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.
A responsible computing framework substitutes real protest imagery with labeled synthetic reproductions from conditional image synthesis to enable privacy-aware analysis of collective action patterns.
Post-stratification plus CUPED cuts required traffic by about 45% for reliable A/B tests on heavy-tailed revenue metrics in ranking experiments.
The paper generates high-fidelity CFD datasets of PWR lower-plenum and core-inlet flow and evaluates ML models for assembly-level mass-flow reconstruction and short-term autoregressive prediction.
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