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
5 Pith papers cite this work. Polarity classification is still indexing.
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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.
citing papers explorer
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MeshTok: Efficient Multi-Scale Tokenization for Scalable PDE Transformers
MeshTok uses AMR-inspired adaptive multiscale tokenization to improve the efficiency-accuracy trade-off of Transformer models for PDEs over uniform-grid baselines.
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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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Protecting and Preserving Protest Dynamics for Responsible Analysis
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.
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Variance Reduction for Heavy-Tailed Monetization Metrics in Ranking Experiments via Post-Stratification
Post-stratification plus CUPED cuts required traffic by about 45% for reliable A/B tests on heavy-tailed revenue metrics in ranking experiments.
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High-fidelity Modeling of Full-scale Pressurized Water Reactor Flow Fields for Machine Learning Applications
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.