| Machine learning–accelerated computational fluid dynamics D Kochkov, JA Smith, A Alieva, Q Wang, MP Brenner, S Hoyer Proceedings of the National Academy of Sciences 118 (21), e2101784118, 2021 | 700 | 2021 |
| A Pareto-efficient combustion framework with submodel assignment for predicting complex flame configurations H Wu, YC See, Q Wang, M Ihme Combustion and Flame 162 (11), 4208-4230, 2015 | 84 | 2015 |
| Regularized deconvolution method for turbulent combustion modeling Q Wang, M Ihme Combustion and Flame 176, 125-142, 2017 | 41 | 2017 |
| A TensorFlow simulation framework for scientific computing of fluid flows on tensor processing units Q Wang, M Ihme, YF Chen, J Anderson Computer Physics Communications 274, 108292, 2022 | 26 | 2022 |
| Deep learning models for predicting wildfires from historical remote-sensing data F Huot, RL Hu, M Ihme, Q Wang, J Burge, T Lu, J Hickey, YF Chen, ... arXiv preprint arXiv:2010.07445, 2020 | 18 | 2020 |
| Assessment of spray combustion models in large-eddy simulations of a polydispersed acetone spray flame Q Wang, T Jaravel, M Ihme Proceedings of the Combustion Institute 37 (3), 3335-3344, 2019 | 18 | 2019 |
| A regularized deconvolution method for turbulent closure modeling in implicitly filtered large-eddy simulation Q Wang, M Ihme Combustion and Flame 204, 341-355, 2019 | 17 | 2019 |
| Mechanism design for aircraft morphing wing Q Wang, Y Chen, H Tang 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials …, 2012 | 16 | 2012 |
| A regularized deconvolution model for sub-grid dispersion in large eddy simulation of turbulent spray flames Q Wang, X Zhao, M Ihme Combustion and Flame 207, 89-100, 2019 | 15 | 2019 |
| X-ray computed tomography for flame-structure analysis of laminar premixed flames E Boigné, P Muhunthan, D Mohaddes, Q Wang, S Sobhani, W Hinshaw, ... Combustion and flame 200, 142-154, 2019 | 13 | 2019 |
| Assessing the mechanisms governing the daytime evolution of marine stratocumulus using large‐eddy simulation LA McMichael, DB Mechem, S Wang, Q Wang, YL Kogan, J Teixeira Quarterly Journal of the Royal Meteorological Society 145 (719), 845-866, 2019 | 12 | 2019 |
| A fidelity adaptive modeling framework for combustion systems based on model trust-region H Wu, YC See, Q Wang, M Ihme 53rd AIAA Aerospace Sciences Meeting, 1381, 2015 | 8 | 2015 |
| Modeling gas dynamic effects in shock-tubes for reaction kinetics measurements K Grogan, Q Wang, M Ihme 53rd AIAA Aerospace Sciences Meeting, 0414, 2015 | 4 | 2015 |
| Neural ideal large eddy simulation: Modeling turbulence with neural stochastic differential equations A Boral, ZY Wan, L Zepeda-Núñez, J Lottes, Q Wang, Y Chen, J Anderson, ... Advances in Neural Information Processing Systems 36, 2024 | 3 | 2024 |
| Distributed data processing for large-scale simulations on cloud T Lu, S Hoyer, Q Wang, L Hu, YF Chen 2021 IEEE International Joint EMC/SI/PI and EMC Europe Symposium, 53-58, 2021 | 3 | 2021 |
| Evaluation of subgrid dispersion models for LES of spray flames XY Zhao, L Esclapez, P Govindaraju, Q Wang, M Ihme CTR Proceedings of the Summer Program, 85-94, 2016 | 3 | 2016 |
| A high-resolution large-eddy simulation framework for wildland fire predictions using TensorFlow Q Wang, M Ihme, RR Linn, YF Chen, V Yang, F Sha, C Clements, ... International journal of wildland fire 32 (12), 1711-1725, 2023 | 2 | 2023 |
| Machine learning accelerated computational fluid dynamics A Alieva, D Kochkov, JA Smith, M Brenner, Q Wang, S Hoyer Proceedings of the National Academy of Sciences USA, 2021 | 2 | 2021 |
| An adaptive combustion framework YC See, H Wu, Q Wang, M Ihme Annual Research Briefs, 2014 | 1 | 2014 |
| Accelerating Large‐Eddy Simulations of Clouds With Tensor Processing Units S Chammas, Q Wang, T Schneider, M Ihme, Y Chen, J Anderson Journal of Advances in Modeling Earth Systems 15 (10), e2023MS003619, 2023 | | 2023 |
Matthias IhmeStanford UniversityVerified email at stanford.edu
