| Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review S Cheng, C Quilodrán-Casas, S Ouala, A Farchi, C Liu, P Tandeo, ... IEEE/CAA Journal of Automatica Sinica 10 (6), 1361-1387, 2023 | 141 | 2023 |
| Data learning: Integrating data assimilation and machine learning C Buizza, CQ Casas, P Nadler, J Mack, S Marrone, Z Titus, C Le Cornec, ... Journal of Computational Science 58, 101525, 2022 | 96 | 2022 |
| Using 137Cs and 210Pbex and other sediment source fingerprints to document suspended sediment sources in small forested catchments in south-central Chile P Schuller, DE Walling, A Iroumé, C Quilodrán, A Castillo, A Navas Journal of environmental radioactivity 124, 147-159, 2013 | 89 | 2013 |
| Med-unic: Unifying cross-lingual medical vision-language pre-training by diminishing bias Z Wan, C Liu, M Zhang, J Fu, B Wang, S Cheng, L Ma, C Quilodrán-Casas, ... Advances in Neural Information Processing Systems 36, 2024 | 64 | 2024 |
| Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic C Quilodrán-Casas, VLS Silva, R Arcucci, CE Heaney, YK Guo, CC Pain Neurocomputing 470, 11-28, 2022 | 55 | 2022 |
| A reduced order deep data assimilation model CQ Casas, R Arcucci, P Wu, C Pain, YK Guo Physica D: Nonlinear Phenomena 412, 132615, 2020 | 53 | 2020 |
| An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics H Gong, S Cheng, Z Chen, Q Li, C Quilodrán-Casas, D Xiao, R Arcucci Annals of nuclear energy 179, 109431, 2022 | 50 | 2022 |
| Parameter flexible wildfire prediction using machine learning techniques: Forward and inverse modelling S Cheng, Y Jin, SP Harrison, C Quilodrán-Casas, IC Prentice, YK Guo, ... Remote Sensing 14 (13), 3228, 2022 | 49 | 2022 |
| Surfactant-laden droplet size prediction in a flow-focusing microchannel: a data-driven approach L Chagot, C Quilodrán-Casas, M Kalli, NM Kovalchuk, MJH Simmons, ... Lab on a Chip 22 (20), 3848-3859, 2022 | 33 | 2022 |
| Data assimilation in the latent space of a neural network M Amendola, R Arcucci, L Mottet, CQ Casas, S Fan, C Pain, P Linden, ... arXiv preprint arXiv:2012.12056, 2020 | 33* | 2020 |
| Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations C Quilodrán-Casas, R Arcucci, L Mottet, Y Guo, C Pain arXiv preprint arXiv:2104.06297, 2021 | 24 | 2021 |
| Adversarially trained LSTMs on reduced order models of urban air pollution simulations C Quilodrán-Casas, R Arcucci, C Pain, Y Guo arXiv preprint arXiv:2101.01568, 2021 | 18 | 2021 |
| T3d: Towards 3d medical image understanding through vision-language pre-training C Liu, C Ouyang, Y Chen, CC Quilodrán-Casas, L Ma, J Fu, Y Guo, ... arXiv preprint arXiv:2312.01529, 2023 | 15 | 2023 |
| Urban air pollution forecasts generated from latent space representations C Quilodrán Casas, R Arcucci, Y Guo ICLR 2020 Workshop on Integration of Deep Neural Models and Differential …, 2020 | 12 | 2020 |
| Fast ocean data assimilation and forecasting using a neural-network reduced-space regional ocean model of the north Brazil current CAQ Casas Imperial College London, 2018 | 11 | 2018 |
| Forecasting tropical cyclones with cascaded diffusion models P Nath, P Shukla, S Wang, C Quilodrán-Casas arXiv preprint arXiv:2310.01690, 2023 | 8 | 2023 |
| Quantifying the temporal variation of the contribution of fine sediment sources to sediment yields from Chilean forested catchments during harvesting operations P Schuller, DE Walling, A Iroumé, C Quilodrán, A Castillo Revista Bosque 42 (2), 231-244, 2021 | 8 | 2021 |
| Latent GAN: using a latent space-based GAN for rapid forecasting of CFD models J Afzali, CQ Casas, R Arcucci International Conference on Computational Science, 360-372, 2021 | 6 | 2021 |
| Reduced order surrogate modelling and Latent Assimilation for dynamical systems S Cheng, C Quilodrán-Casas, R Arcucci International Conference on Computational Science, 31-44, 2022 | 5 | 2022 |
| A domain decomposition reduced order model with data assimilation (dd-roda) R Arcucci, CQ Casas, D Xiao, L Mottet, F Fang, P Wu, C Pain, YK Guo Parallel Computing: Technology Trends, 189-198, 2020 | 5 | 2020 |
Rossella ArcucciAssociate Professor (Senior Lecturer), Imperial College Londonبريد إلكتروني تم التحقق منه على imperial.ac.uk
