| On helium cluster dynamics in tungsten plasma facing components of fusion devices SI Krasheninnikov, T Faney, BD Wirth Nuclear Fusion 54 (7), 073019, 2014 | 64 | 2014 |
| Spatially dependent cluster dynamics modeling of microstructure evolution in low energy helium irradiated tungsten T Faney, BD Wirth Modelling and Simulation in Materials Science and Engineering 22 (6), 065010, 2014 | 51 | 2014 |
| Spatially dependent cluster dynamics model of He plasma surface interaction in tungsten for fusion relevant conditions T Faney, SI Krasheninnikov, BD Wirth Nuclear Fusion 55 (1), 013014, 2014 | 36 | 2014 |
| Numerical simulations of tungsten under helium irradiation T Faney University of California, Berkeley, 2013 | 13 | 2013 |
| PTFlash: A vectorized and parallel deep learning framework for two-phase flash calculation J Qu, T Faney, JC de Hemptinne, S Yousef, P Gallinari Fuel 331, 125603, 2023 | 8 | 2023 |
| HMOE: hypernetwork-based mixture of experts for domain generalization J Qu, T Faney, Z Wang, P Gallinari, S Yousef, JC de Hemptinne arXiv preprint arXiv:2211.08253, 2022 | 4 | 2022 |
| Machine learning model predicting hydrothermal dolomitisation for future coupling of basin modelling and geochemical simulations N Collard, T Faney, PA Teboul, P Bachaud, MC Cacas-Stentz, C Gout Chemical Geology 637, 121676, 2023 | 2 | 2023 |
| An Implicit GNN Solver for Poisson-like problems M Nastorg, MA Bucci, T Faney, JM Gratien, G Charpiat, M Schoenauer arXiv preprint arXiv:2302.10891, 2023 | 2 | 2023 |
| In Situ TEM Studies of Microstructure Evolution Under Ion Irradiation for Nuclear Engineering Applications D Kaoumi, AT Motta, M Kirk, T Faney, B Wirth, J Bentley Microscopy and Microanalysis 16 (S2), 1606-1607, 2010 | 2 | 2010 |
| Clustering-Enhanced Deep Learning Method for Computation of Full Detailed Thermochemical States via Solver-Based Adaptive Sampling X Chen, C Mehl, T Faney, F Di Meglio Energy & Fuels 37 (18), 14222-14239, 2023 | 1 | 2023 |
| Hybrid Newton method for the acceleration of well event handling in the simulation of CO2 storage using supervised learning A Lechevallier, S Desroziers, T Faney, E Flauraud, F Nataf Available at SSRN 4696416, 2023 | 1 | 2023 |
| Robust resolution of single-phase chemical equilibrium using parametrization and Cartesian representation techniques IB Gharbia, C Cancès, T Faney, M Jonval, QH Tran | 1 | 2023 |
| Ds-gps: A deep statistical graph poisson solver (for faster cfd simulations) M Nastorg, M Schoenauer, G Charpiat, T Faney, JM Gratien, MA Bucci arXiv preprint arXiv:2211.11763, 2022 | 1 | 2022 |
| Machine learning model for gas-liquid interface reconstruction in CFD numerical simulations T Nakano, AM Bucci, JM Gratien, T Faney, G Charpiat arXiv preprint arXiv:2207.05684, 2022 | 1 | 2022 |
| Discontinuous kinetics models for reactive transport problems B Hamlat, J Erhel, A Michel, T Faney CMWR 2018 Congress, 2018 | 1 | 2018 |
| Modélisation des systèmes cinétiques limités B Hamlat, J Erhel, A Michel, T Faney SMAI 2017-8e Biennale Française des Mathématiques Appliquées et Industrielles, 2017 | 1 | 2017 |
| Multi-Level GNN Preconditioner for Solving Large Scale Problems M Nastorg, JM Gratien, T Faney, MA Bucci, G Charpiat, M Schoenauer arXiv preprint arXiv:2402.08296, 2024 | | 2024 |
| A neural-network-based hydrothermal dolomitization model for future coupling with basin modeling in the energy transition context N Collard, PA Teboul, T Faney, MC Cacas-Stentz, C Gout Goldschmidt 2023 Conference, 2023 | | 2023 |
| DS-GPS: A Deep Statistical Graph Poisson Solver M Nastorg, M Schoenauer, G Charpiat, T Faney, JM Gratien, MA Bucci NeurIPS 2022-Machine Learning and the Physical Sciences, workshop, 2022 | | 2022 |
| PTFlash: A deep learning framework for isothermal two-phase equilibrium calculations J Qu, T Faney, JC de Hemptinne, S Yousef, P Gallinari arXiv preprint arXiv:2205.03090, 2022 | | 2022 |
