Physics-Informed Convolutional Neural Networks for Corruption Removal on Dynamical Systems. - Daniel Kelshaw; Luca Magri (CoRR, 2022)

Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical Systems. - Daniel Kelshaw; Georgios Rigas; Luca Magri (CoRR, 2022)

Short and Straight: Geodesics on Differentiable Manifolds. - Daniel Kelshaw; Luca Magri (CoRR, 2023)

Uncovering solutions from data corrupted by systematic errors: A physics-constrained convolutional neural network approach. - Daniel Kelshaw; Luca Magri (CoRR, 2023)

Super-resolving sparse observations in partial differential equations: A physics-constrained convolutional neural network approach. - Daniel Kelshaw; Luca Magri (CoRR, 2023)

Manifold-augmented Eikonal Equations: Geodesic Distances and Flows on Differentiable Manifolds. - Daniel Kelshaw; Luca Magri (CoRR, 2023)

Physics-constrained convolutional neural networks for inverse problems in spatiotemporal partial differential equations. - Daniel Kelshaw; Luca Magri (CoRR, 2024)

Computing distances and means on manifolds with a metric-constrained Eikonal approach. - Daniel Kelshaw; Luca Magri (CoRR, 2024)

Proper Latent Decomposition. - Daniel Kelshaw; Luca Magri (CoRR, 2024)