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ForcingDAS is introduced, a unified and robust DA framework that learns a joint-trajectory prior instead of frame-to-frame transitions that allows it to capture long-horizon temporal dependencies and reduce error accumulation.
Recovering Partial Differential Equation (PDE) coefficient fields from extremely sparse observations is a severely ill-posed inverse problem for which generative machine learning methods (e.g., diffusion models) have become a leading way to encode the prior. Recent state-of-the-art diffusion solvers lift these priors t...
This paper develops a differentiable framework for multi-objective MaxCut by combining an adjacency-based quadratic formulation with linear scalarization, thereby reducing the problem to a preference-conditioned single-objective signed-weight MaxCut problem.
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