Distilled one-step consistency model from optimal-transport flow-matching teacher reconstructs high-fidelity dynamical system flows from low-fidelity data with 12x speedup, half the parameters, and 23.1% better SSIM than scratch-trained baselines.
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3 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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An ALE-consistent GNO-ViT and LSTM framework with boundary correction and two-stage training achieves accurate phase-consistent long-term FSI predictions on a flexible beam benchmark with good generalization to inlet variations.
DiffUNet^2 is a bidirectional conditional diffusion model integrated with visual tools for probabilistic exploration of scientific time series across five evaluated datasets.
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Physical Fidelity Reconstruction via Improved Consistency-Distilled Flow Matching for Dynamical Systems
Distilled one-step consistency model from optimal-transport flow-matching teacher reconstructs high-fidelity dynamical system flows from low-fidelity data with 12x speedup, half the parameters, and 23.1% better SSIM than scratch-trained baselines.
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An ALE-Consistent Graph Neural Operator-Transformer Framework for Fluid-Structure Interaction
An ALE-consistent GNO-ViT and LSTM framework with boundary correction and two-stage training achieves accurate phase-consistent long-term FSI predictions on a flexible beam benchmark with good generalization to inlet variations.
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DiffUNet^2: Bidirectional Prediction, Probabilistic Generation and Collaborative Visual Discovery for Scientific Data
DiffUNet^2 is a bidirectional conditional diffusion model integrated with visual tools for probabilistic exploration of scientific time series across five evaluated datasets.