| 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 | 247 | 2023 |
| Solving the wave equation with physics-informed deep learning B Moseley, A Markham, T Nissen-Meyer arXiv preprint arXiv:2006.11894, 2020 | 172 | 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 | 112 | 2020 |
| Fast approximate simulation of seismic waves with deep learning B Moseley, A Markham, T Nissen-Meyer arXiv preprint arXiv:1807.06873, 2018 | 79 | 2018 |
| Peering into lunar permanently shadowed regions with deep learning VT Bickel, B Moseley, I Lopez-Francos, M Shirley Nature Communications 12 (1), 5607, 2021 | 37 | 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 | 36 | 2024 |
| 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 | 36 | 2021 |
| 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 | 21 | 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 | 18 | 2022 |
| 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 | 18 | 2022 |
| Physics-informed machine learning: from concepts to real-world applications B Moseley University of Oxford, 2022 | 18 | 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 | 14 | 2022 |
| So, what is a physics-informed neural network B Moseley | 7 | 2021 |
| 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 | 6 | 2020 |
| 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 | 5 | 2021 |
| Bayesian optimisation for variational quantum eigensolvers B Moseley, M Osborne, S Benjamin | 5 | 2018 |
| 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 | 2 | 2023 |
| 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 |
| History-Matching of Imbibition Flow in Multiscale Fractured Porous Media Using Physics-Informed Neural Networks (PINNs) J Abbasi, B Moseley, T Kurotori, AD Jagtap, AR Kovscek, A Hiorth, ... arXiv preprint arXiv:2410.20801, 2024 | 1 | 2024 |
Tarje Nissen-MeyerUniversity of ExeterПідтверджена електронна адреса в alumni.princeton.edu