Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations B Moseley, A Markham, T Nissen-Meyer Advances in Computational Mathematics 49 (4), 62, 2023 | 159 | 2023 |
Solving the wave equation with physics-informed deep learning B Moseley, A Markham, T Nissen-Meyer arXiv preprint arXiv:2006.11894, 2020 | 143 | 2020 |
Deep learning for fast simulation of seismic waves in complex media B Moseley, T Nissen-Meyer, A Markham Solid Earth 11 (4), 1527-1549, 2020 | 96 | 2020 |
Fast approximate simulation of seismic waves with deep learning B Moseley, A Markham, T Nissen-Meyer arXiv preprint arXiv:1807.06873, 2018 | 75 | 2018 |
Extreme low-light environment-driven image denoising over permanently shadowed lunar regions with a physical noise model B Moseley, V Bickel, IG López-Francos, L Rana Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 30 | 2021 |
Peering into lunar permanently shadowed regions with deep learning VT Bickel, B Moseley, I Lopez-Francos, M Shirley Nature communications 12 (1), 5607, 2021 | 26 | 2021 |
Cryogeomorphic characterization of shadowed regions in the Artemis exploration zone VT Bickel, B Moseley, E Hauber, M Shirley, JP Williams, DA Kring Geophysical Research Letters 49 (16), e2022GL099530, 2022 | 16 | 2022 |
Seismic localization of elephant rumbles as a monitoring approach M Reinwald, B Moseley, A Szenicer, T Nissen-Meyer, S Oduor, F Vollrath, ... Journal of the Royal Society Interface 18 (180), 20210264, 2021 | 16 | 2021 |
Multilevel domain decomposition-based architectures for physics-informed neural networks V Dolean, A Heinlein, S Mishra, B Moseley Computer Methods in Applied Mechanics and Engineering 429, 117116, 2024 | 14 | 2024 |
Seismic savanna: machine learning for classifying wildlife and behaviours using ground‐based vibration field recordings A Szenicer, M Reinwald, B Moseley, T Nissen‐Meyer, Z Mutinda Muteti, ... Remote Sensing in Ecology and Conservation 8 (2), 236-250, 2022 | 12 | 2022 |
Physics-informed machine learning: from concepts to real-world applications B Moseley University of Oxford, 2022 | 11 | 2022 |
Finite basis physics-informed neural networks as a Schwarz domain decomposition method V Dolean, A Heinlein, S Mishra, B Moseley International Conference on Domain Decomposition Methods, 165-172, 2022 | 8 | 2022 |
Unsupervised learning for thermophysical analysis on the lunar surface B Moseley, V Bickel, J Burelbach, N Relatores The Planetary Science Journal 1 (2), 32, 2020 | 5 | 2020 |
Bayesian optimisation for variational quantum eigensolvers B Moseley, M Osborne, S Benjamin | 5 | 2018 |
Single image super-resolution with uncertainty estimation for lunar satellite images JI Delgado-Centeno, P Harder, B Moseley, V Bickel, S Ganju, ... NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications, 2021 | 4 | 2021 |
Unsupervised learning for thermal anomaly detection on the lunar surface B Moseley, V Bickel, J Burelbach, N Relatores, D Angerhausen, ... Second Workshop on Machine Learning and the Physical Sciences, NeurIPS 2019, 2019 | 2 | 2019 |
Post-stack 1-D Based Broadband Processing-A Simple and Efficient Method for Removing the Ghost B Moseley 77th EAGE Conference and Exhibition 2015 2015 (1), 1-5, 2015 | 2 | 2015 |
Superresolution of Lunar Satellite Images for Enhanced Robotic Traverse Planning: Maximizing the Value of Existing Data Products for Space Robotics JI Delgado-Centeno, P Harder, V Bickel, B Moseley, F Kalaitzis, S Ganju, ... IEEE Robotics & Automation Magazine, 2023 | 1 | 2023 |
Thermophysical Change Detection on the Moon with the Lunar Reconnaissance Orbiter Diviner sensor S Bucci, JID Centeno, B Gaffinet, Z Liang, V Bickel, B Moseley, ... Proceedings of the Neural Information Processing Systems" Machine Learning …, 2022 | 1 | 2022 |
Scaling physics-informed neural networks to large domains by using domain decomposition B Moseley, A Markham, T Nissen-Meyer The Symbiosis of Deep Learning and Differential Equations, 2021 | 1 | 2021 |