2023
Kelshaw, Daniel., Magri, Luca. 'Manifold-augmented Eikonal Equations: Geodesic Distances and Flows on Differentiable Manifolds'. arXiv, 2023. https://arxiv.org/abs/2310.06157.
Kelshaw, Daniel., Magri, Luca. 'Super-resolving sparse observations in partial differential equations: A physics-constrained convolutional neural network approach'. arXiv, 2023. https://arxiv.org/abs/2306.10990.
Kelshaw, Daniel., Magri, Luca. 'Uncovering solutions from data corrupted by systematic errors: A physics-constrained convolutional neural network approach'. arXiv, 2023. https://arxiv.org/abs/2306.04600.
Kelshaw, Daniel., Magri, Luca. 'Short and Straight: Geodesics on Differentiable Manifolds'. arXiv, 2023. https://arxiv.org/abs/2305.15228.
2022
Kelshaw, Daniel., Rigas, Georgios., Magri, Luca. 'Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical Systems', NeurIPS Machine Learning and the Physical Sciences Workshop, 2022. https://arxiv.org/abs/2210.17319.
Kelshaw, Daniel., Magri, Luca. 'Physics-Informed Convolutional Neural Networks for Corruption Removal on Dynamical Systems', NeurIPS Machine Learning and the Physical Sciences Workshop, 2022. https://arxiv.org/abs/2210.16215.
Talks
Magri, Luca., Kelshaw, Daniel., Doan, N.A.K. 'What is machine learning learning? Autoencoders for reduced-order modelling of turbulence.', IACM Computational Fluids Conference, 2023.
Kelshaw, Daniel., Magri, Luca. 'Dealing with faulty sensors: a physics-informed convolutional neural network approach for recovering solutions to governing equations', IACM Computational Fluids Conference, 2023.
Kelshaw, Daniel., Magri, Luca. 'Extracting Navier-Stokes solutions from noisy data with physics-constrained convolutional neural networks', Bulletin of the American Physical Society, 2022.
Kelshaw, Daniel., Mauceri, Steffan., Lu, Steven., Xu, Liang., Saatchi, Sassan. 'Gaussian Processes for Prediction and Uncertainty Quantification of Global Vegitation Structure from Active Satellite Sensors', American Geophysical Society, 2021