Qing Wang
Cited by
Cited by
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
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
Regularized deconvolution method for turbulent combustion modeling
Q Wang, M Ihme
Combustion and Flame 176, 125-142, 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
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
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
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
Mechanism design for aircraft morphing wing
Q Wang, Y Chen, H Tang
53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials …, 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
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
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
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
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
Modeling gas dynamic effects in shock-tubes for reaction kinetics measurements
K Grogan, Q Wang, M Ihme
53rd AIAA Aerospace Sciences Meeting, 0414, 2015
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
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
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
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
BLASTNet simulation dataset
WT Chung, M Ihme, KS Jung, JH Chen, J Guo, D Brouzet, M Talei, ...
Zenodo, 2024
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
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