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This paper introduces DiMS, a dynamical system sampler that guarantees exact sampling from the submanifold of minimum loss solutions in neural networks, enabling better uncertainty quantification in Bayesian inference.
This paper investigates parallel-in-time algorithms for training recurrent neural networks in dynamical systems reconstruction, proposing GTF-DEER that enables stable learning over long sequences and improves reconstruction accuracy.
This paper introduces Dynamical Physics-Modeled Neural Networks (DynPMNNs), a continuous-time deep learning architecture where hidden layers are defined by ordinary differential equations. It presents a biologically inspired approach grounded in Reproducing Kernel Banach Spaces, demonstrating competitive performance on the California Housing dataset with fewer parameters than standard Neural ODEs.
This paper introduces MeLISA, a latent-free autoregressive generative surrogate for forecasting high-dimensional physical dynamics that uses pixel-space MeanFlow to achieve efficient one-step generation. It demonstrates superior long-horizon statistical accuracy and inference speed compared to neural operators on turbulent flow benchmarks.