PEIL learns unobservable parameters by embedding them in a physics-based reconstruction loop, outperforming supervised baselines with ground-truth access while enabling zero-shot generalization and major data reduction in wireless and MRI tasks.
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A generative solver separates data-driven prior learning from inference-time enforcement of conservation laws using martingale-regularized score matching and physics-informed sampling for stable field reconstruction.
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Discovery of unobservable parameters via physical embedding
PEIL learns unobservable parameters by embedding them in a physics-based reconstruction loop, outperforming supervised baselines with ground-truth access while enabling zero-shot generalization and major data reduction in wireless and MRI tasks.
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Physics-Informed Generative Solver: Bridging Data-Driven Priors and Conservation Laws for Stable Spatiotemporal Field Reconstruction
A generative solver separates data-driven prior learning from inference-time enforcement of conservation laws using martingale-regularized score matching and physics-informed sampling for stable field reconstruction.