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Comparing Manifold-Constrained Gaussian Processes and Physics-Inspired Neural Networks for Parameter Inference in Epidemiological Models
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Kernel and Lengthscale Selection on the Performance of the Sparse Cholesky Factorization Algorithm
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Reducing Operator Complexity in Algebraic Multigrid with Machine Learning Approaches
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Efficient Preconditioner for Covariance Matrices Using Geometric Information
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Improving Multigrid Methods with Deep Neural Networks
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Generating Graphs with Deep Learning and Graph Theory
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Physics-informed Deep Neural Networks for High Dimensional Integration
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Optimal Control Approaches for Designing Neural Ordinary Differential Equations
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Fast Gaussian Process Solver
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