piDMD learns a single parameter-affine Koopman surrogate ROM from training samples at multiple parameters to predict dynamics at unseen parameters with improved robustness over interpolation baselines.
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Photonic accelerators hit a topology-driven Utilization Wall; symmetric grids improve utilization up to 6X and cut memory access over 40% versus linear layouts.
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Parametric Interpolation of Dynamic Mode Decomposition for Predicting Nonlinear Systems
piDMD learns a single parameter-affine Koopman surrogate ROM from training samples at multiple parameters to predict dynamics at unseen parameters with improved robustness over interpolation baselines.
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Towards Topology-Aware Very Large-Scale Photonic AI Accelerators
Photonic accelerators hit a topology-driven Utilization Wall; symmetric grids improve utilization up to 6X and cut memory access over 40% versus linear layouts.