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Daniel Kelshaw

Computing distances and means on manifolds with a metric-constrained Eikonal approach

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Apr 12, 2024
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Physics-constrained convolutional neural networks for inverse problems in spatiotemporal partial differential equations

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Feb 01, 2024
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Manifold-augmented Eikonal Equations: Geodesic Distances and Flows on Differentiable Manifolds

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Oct 09, 2023
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Super-resolving sparse observations in partial differential equations: A physics-constrained convolutional neural network approach

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Jun 19, 2023
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Uncovering solutions from data corrupted by systematic errors: A physics-constrained convolutional neural network approach

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Jun 19, 2023
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Short and Straight: Geodesics on Differentiable Manifolds

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May 24, 2023
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Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical Systems

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Nov 07, 2022
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Physics-Informed Convolutional Neural Networks for Corruption Removal on Dynamical Systems

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Nov 07, 2022
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