M³ partitions space by physical variation using multi-scale Morton ordering to balance training measures, yielding up to 4.7× lower error on industrial volumetric datasets and outperforming higher-resolution training even after aggressive subsampling.
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Di-BiLPS combines a variational autoencoder, latent diffusion, and contrastive learning to achieve state-of-the-art accuracy on PDE problems with as little as 3% observations while supporting zero-shot super-resolution and lower computational cost.
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M$^3$: Reframing Training Measures for Discretized Physical Simulations
M³ partitions space by physical variation using multi-scale Morton ordering to balance training measures, yielding up to 4.7× lower error on industrial volumetric datasets and outperforming higher-resolution training even after aggressive subsampling.
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Di-BiLPS: Denoising induced Bidirectional Latent-PDE-Solver under Sparse Observations
Di-BiLPS combines a variational autoencoder, latent diffusion, and contrastive learning to achieve state-of-the-art accuracy on PDE problems with as little as 3% observations while supporting zero-shot super-resolution and lower computational cost.