Recent Advances in Probabilistic Scientific Machine Learning for Chaotic Dynamical Systems

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Deep Uncertainty Quantification for Dynamics Learning and Decision-Making

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Two Tales, One Resolution: Physics-Informed Inference Time Scaling and Precondition

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The mean-squared-error is not enough: Improved prediction of chaotic dynamical systems with scientific machine learning

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Toward physical generative modeling

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Transport- and Measure-Theoretic Approaches for Dynamical System Modeling

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Quantum mechanical closure of partial differential equations with symmetries

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On Over-Parametrized Models and Sobolev Training

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