| Faster than real time tsunami warning with associated hazard uncertainties D Giles, D Gopinathan, S Guillas, F Dias Frontiers in Earth Science 8, 560, 2021 | 33 | 2021 |
| The VOLNA-OP2 tsunami code (version 1.5) IZ Reguly, D Giles, D Gopinathan, L Quivy, JH Beck, MB Giles, S Guillas, ... Geoscientific Model Development 11 (11), 4621-4635, 2018 | 23 | 2018 |
| Performance analysis of Volna-OP2 – massively parallel code for tsunami modelling D Giles, E Kashdan, DM Salmanidou, S Guillas, F Dias Computers & Fluids 209, 104649, 2020 | 14 | 2020 |
| Multilevel Bayesian Quadrature K Li, D Giles, T Karvonen, S Guillas, FX Briol International Conference on Artificial Intelligence and Statistics, 1845-1868, 2023 | 5 | 2023 |
| Meteotsunamis and other anomalous “tidal surge” events in Western Europe in Summer 2022 E Renzi, C Bergin, T Kokina, DS Pelaez-Zapata, D Giles, F Dias Physics of Fluids 35 (046605), 2023 | 4 | 2023 |
| Modelling with Volna-OP2—Towards Tsunami Threat Reduction for the Irish Coastline. D Giles, B McConnell, F Dias Geosciences 10 (226), 2020 | 2 | 2020 |
| Automated approaches for capturing localised tsunami response‐Application to the French coastlines D Giles, A Gailler, F Dias Journal of Geophysical Research: Oceans 127 (6), e2022JC018467, 2022 | 1 | 2022 |
| Uncertainty Quantification of Pre-Trained and Fine-Tuned Surrogate Models using Conformal Prediction V Gopakumar, A Gray, J Oskarsson, L Zanisi, S Pamela, D Giles, ... arXiv e-prints, arXiv: 2408.09881, 2024 | | 2024 |
| Valid Error Bars for Neural Weather Models using Conformal Prediction V Gopakumar, J Oskarrson, A Gray, L Zanisi, S Pamela, D Giles, ... arXiv preprint arXiv:2406.14483, 2024 | | 2024 |
| Embedding machine-learnt sub-grid variability improves climate model biases D Giles, J Briant, CJ Morcrette, S Guillas arXiv, 2024 | | 2024 |
| Transatlantic Data Science Academy Project. Phase 1: Scoping and Shaping SL Dance, A Barber, C Brierley, L Chapman, M Collins, J Crook, ... https://zenodo.org/records/11191276, 2024 | | 2024 |
| Scalable Data Assimilation with Message Passing O Key, S Takao, D Giles, MP Deisenroth arXiv:2404.12968, 2024 | | 2024 |
| A collaborative hackathon to investigate climate change and extreme weather impacts in justice and insurance settings JD Macholl, H Roberts, H Steptoe, S Sun, M Angus, C Davenport, ... Weather, 2024 | | 2024 |
| ParticleDA.jl v.1.0: a distributed particle-filtering data assimilation package D Giles, MM Graham, M Giordano, T Koskela, A Beskos, S Guillas Geosci. Model Dev. 17, 2427–2445, 2024 | | 2024 |
| A Hybrid Machine Learning Climate Simulation Using High Resolution Convection Modelling D Giles, J Briant, C Morcrette, S Guillas SIAM Conference on Uncertainty Quantification (UQ24), 2024 | | 2024 |
| Time-dependent influence metric for cascade dynamics on networks JP Gleeson, A Cassidy, D Giles, A Faqeeh https://arxiv.org/abs/2401.16978, 2024 | | 2024 |
| Fusion of a Machine Learning and Climate Model to Embed High Resolution Variability into a Coarse Resolution Climate Simulation D Giles, C Morcrette, S Guillas SIAM Conference on Mathematical & Computational Issues in the Geosciences (GS23), 2023 | | 2023 |
| Development of fast computational methods for tsunami modelling D Giles University College Dublin. School of Mathematics and Statistics, 2021 | | 2021 |
| Comparison of local amplification factors for fast forecast coastal tsunami amplitude modeling based on the extended Green's law A Gailler, D Giles EGU General Assembly Conference Abstracts, 7412, 2020 | | 2020 |
Frederic DiasProfessor of Applied Mathematics, University College DublinVerifisert e-postadresse på ucd.ie