arXiv, 2024
Recommended citation: Gopakumar, V., et al.: Uncertainty Quantification of Pre-Trained and Fine-Tuned Surrogate Models using Conformal Prediction, 2024. https://doi.org/10.48550/arXiv.2408.09881
arXiv, 2024
Recommended citation: Gopakumar, V., et al.: Valid Error Bars for Neural Weather Models using Conformal Prediction, 2024. https://doi.org/10.48550/arXiv.2406.14483
arXiv, 2024
Recommended citation: Giles , D., et al.: Embedding machine-learnt sub-grid variability improves climate model biases, 2024. https://doi.org/10.48550/arXiv.2406.09551
Weather, 2024
Recommended citation: Macholl, J.D., et al.: A collaborative hackathon to investigate climate change and extreme weather impacts in justice and insurance settings, 2024. https://doi.org/10.1002/wea.4560
Zenodo, 2024
Recommended citation: Dance , S., et al.: Transatlantic Data Science Academy Project. Phase 1: Scoping and Shaping, 2024. https://doi.org/10.5281/zenodo.11191276
arXiv, 2024
Recommended citation: Key, O., et al.: Scalable Data Assimilation with Message Passing, 2024. https://doi.org/10.48550/arXiv.2404.12968
Geoscientific Model Development, 2024
Recommended citation: Giles, D., Graham, M. M., Giordano, M., Koskela, T., Beskos, A., and Guillas, S.: ParticleDA.jl v.1.0: a distributed particle-filtering data assimilation package, Geosci. Model Dev., 17, 2427–2445, 2024. https://doi.org/10.5194/gmd-17-2427-2024
Physical Review E (under review), 2024
Recommended citation: Gleeson, J., Cassidy, A., Giles, D. and Faqeeh, A.: Time-dependent influence metric for cascade dynamics on networks, 2024. https://doi.org/10.48550/arXiv.2401.16978
Physics of Fluids, 2023
Recommended citation: E. Renzi, C. Bergin, T. Kokina, D. S. Pelaez-Zapata, D. Giles and F. Dias: Meteotsunamis and other anomalous “tidal surge” events in Western Europe in Summer 2022. Physics of Fluids, 35, 4, 2023 https://doi.org/10.1063/5.0139220
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, 2023
Recommended citation: Kaiyu, L., Giles, D., Karvonen, T., Guillas, S. and Briol, F.X.: Multilevel Bayesian Quadrature, Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, 206, 1845--1868, 2023. https://proceedings.mlr.press/v206/li23a.html
Journal of Geophysical Research: Oceans, 2022
Recommended citation: Giles, D., Gailler, A., & Dias, F.: Automated approaches for capturing localized tsunami response—Application to the French coastlines. Journal of Geophysical Research: Oceans, 127, 2022 https://doi.org/10.1029/2022JC018467
Frontiers in Earth Sciences, 2021
Recommended citation: Giles, D., Gopinathan, D., Guillas, S., and Dias, F.: Faster Than Real Time Tsunami Warning with Associated Hazard Uncertainties, Front. Earth Sci., 8, 226, 2021. https://doi.org/10.3389/feart.2020.597865
Computers & Fluids, 2020
Recommended citation: Giles, D., Kashdan, E., Salmanidou, M. D., Guillas, S., and Dias, F.: Performance analysis of Volna-OP2 – massively parallel code for tsunami modelling, Computers & Fluids, 209, 104649, 2020. https://doi.org/10.1016/j.compfluid.2020.104649
Geosciences, 2020
Recommended citation: Giles, D., McConnell, B., and Dias, F.: Modelling with Volna-OP2—Towards Tsunami Threat Reduction for the Irish Coastline, Geosciences, 10(6), 226, 2020. https://doi.org/10.3390/geosciences10060226
Geoscientific Model Development, 2018
Recommended citation: Reguly, I. Z., Giles, D., Gopinathan, D., Quivy, L., Beck, J. H., Giles, M. B., Guillas, S., and Dias, F.: The VOLNA-OP2 tsunami code (version 1.5), Geosci. Model Dev., 11, 4621–4635, 2018. https://doi.org/10.5194/gmd-11-4621-2018