| Digital twins that learn and correct themselves B Moya, A Badías, I Alfaro, F Chinesta, E Cueto International Journal for Numerical Methods in Engineering 123 (13), 3034-3044, 2022 | 65 | 2022 |
| Learning slosh dynamics by means of data B Moya, D González, I Alfaro, F Chinesta, E Cueto Computational Mechanics 64, 511-523, 2019 | 56 | 2019 |
| A graph convolutional autoencoder approach to model order reduction for parametrized PDEs F Pichi, B Moya, JS Hesthaven Journal of Computational Physics 501, 112762, 2024 | 52 | 2024 |
| Physically sound, self-learning digital twins for sloshing fluids B Moya, I Alfaro, D Gonzalez, F Chinesta, E Cueto PLoS One 15 (6), e0234569, 2020 | 38 | 2020 |
| Physics perception in sloshing scenes with guaranteed thermodynamic consistency B Moya, A Badias, D Gonzalez, F Chinesta, E Cueto IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (2), 2136-2150, 2022 | 19 | 2022 |
| A thermodynamics-informed active learning approach to perception and reasoning about fluids B Moya, A Badías, D González, F Chinesta, E Cueto Computational Mechanics 72 (3), 577-591, 2023 | 13 | 2023 |
| Learning physics from data: a thermodynamic interpretation F Chinesta, E Cueto, M Grmela, B Moya, M Pavelka, M Šípka Workshop on Joint Structures and Common Foundations of Statistical Physics …, 2020 | 11 | 2020 |
| Computational Sensing, Understanding, and Reasoning: An Artificial Intelligence Approach to Physics-Informed World Modeling B Moya, A Badías, D González, F Chinesta, E Cueto Archives of Computational Methods in Engineering 31 (4), 1897-1914, 2024 | 5 | 2024 |
| Physics-informed reinforcement learning for perception and reasoning about fluids B Moya, A Badias, D Gonzalez, F Chinesta, E Cueto arXiv e-prints, arXiv: 2203.05775, 2022 | 4 | 2022 |
| Harnessing Hybrid Digital Twinning for Decision-Support in Smart Infrastructures H Liang, B Moya, E Seah, ANK Weng, D Baillargeat, J Joerin, X Zhang, ... Engineering Archive, 2024 | 1 | 2024 |
| Thermodynamics-informed super-resolution of scarce temporal dynamics data C Bermejo-Barbanoj, B Moya, A Badías, F Chinesta, E Cueto arXiv preprint arXiv:2402.17506, 2024 | 1 | 2024 |
| Teoría de estructuras para arquitectos E Cueto, D González, B Moya Prensas de la Universidad de Zaragoza, 2023 | 1 | 2023 |
| Resilience-based post disaster recovery optimization for infrastructure system via Deep Reinforcement Learning H Liang, B Moya, F Chinesta, E Chatzi arXiv preprint arXiv:2410.18577, 2024 | | 2024 |
| Uso de la Estática Gráfica Computacional en el aprendizaje del diseño estructural B Moya, D González, I Alfaro, EC Prendes Conference proceedings CINEVIDU 2024: 8th International Virtual Conference …, 2024 | | 2024 |
| Superresolución con redes neuronales informadas por la termodinámica en problemas fluidodinámicos CB Barbanoj, B Moya, A Badías, F Chinesta, E Cueto Jornada de Jóvenes Investigadores del I3A 11, 2023 | | 2023 |
| Thermodynamics of the machine learning of physical phenomena B Moya, D González, F Chinesta, EC Prendes Congress on Numerical Methods in Engineering CMN 2022 (2022. Las Palmas de …, 2022 | | 2022 |
| Reinforcement learning for physically sound fluid dynamics correction B Moya, A Badias, D González, F Chinesta, E Cueto Congress on Numerical Methods in Engineering CMN 2022 (2022. Las Palmas de …, 2022 | | 2022 |
| Data Learning of Fluid Dynamics for Physically Informed Digital Twins B Moya, I Alfaro, D González, F Chinesta, E Cueto Jornada de Jóvenes Investigadores del I3A 8, 2020 | | 2020 |
| Physics informed Generative Adversarial Networks for interactive structural shell design B Moya, F Chinesta, E Cueto | | |
| Deep learning of fluid dynamics from free surface data for full state reconstruction and correction B Moya, A Badıas, Q Hernández, D González, I Alfaro, F Chinesta, ... | | |
Francisco (Paco) ChinestaArts et Metiers Paris / CNRS@CREATE Singapore / ESI, RTE, SKF CHAIRS / Spanish Royal Acad. EngrgBestätigte E-Mail-Adresse bei ensam.eu
