热门出版物
标题/作者年份
Turbulence modeling in the age of data
K Duraisamy, G Iaccarino, H Xiao
Annual review of fluid mechanics 51, 357-377
2019
Digital twin: Values, challenges and enablers from a modeling perspective
A Rasheed, O San, T Kvamsdal
Ieee Access 8, 21980-22012
2020
Informed machine learning–a taxonomy and survey of integrating prior knowledge into learning systems
L Von Rueden, S Mayer, K Beckh, B Georgiev, S Giesselbach, R Heese, ...
IEEE Transactions on Knowledge and Data Engineering 35 (1), 614-633
2021
Integrating physics-based modeling with machine learning: A survey
J Willard, X Jia, S Xu, M Steinbach, V Kumar
arXiv preprint arXiv:2003.04919 1 (1), 1-34
2020
Quantification of model uncertainty in RANS simulations: A review
H Xiao, P Cinnella
Progress in Aerospace Sciences 108, 1-31
2019
Subgrid modelling for two-dimensional turbulence using neural networks
R Maulik, O San, A Rasheed, P Vedula
Journal of Fluid Mechanics 858, 122-144
2019
Fast flow field prediction over airfoils using deep learning approach
V Sekar, Q Jiang, C Shu, BC Khoo
Physics of Fluids 31 (5)
2019
Perspective on machine learning for advancing fluid mechanics
MP Brenner, JD Eldredge, JB Freund
Physical Review Fluids 4 (10), 100501
2019
Enforcing analytic constraints in neural networks emulating physical systems
T Beucler, M Pritchard, S Rasp, J Ott, P Baldi, P Gentine
Physical Review Letters 126 (9), 098302
2021
Predictive large-eddy-simulation wall modeling via physics-informed neural networks
XIA Yang, S Zafar, JX Wang, H Xiao
Physical Review Fluids 4 (3), 034602
2019
Predictions of turbulent shear flows using deep neural networks
PA Srinivasan, L Guastoni, H Azizpour, P Schlatter, R Vinuesa
Physical Review Fluids 4 (5), 054603
2019
Integrating scientific knowledge with machine learning for engineering and environmental systems
J Willard, X Jia, S Xu, M Steinbach, V Kumar
ACM Computing Surveys 55 (4), 1-37
2022
Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
K Fukami, K Fukagata, K Taira
Journal of Fluid Mechanics 909, A9
2021
Driven by data or derived through physics? a review of hybrid physics guided machine learning techniques with cyber-physical system (cps) focus
R Rai, CK Sahu
IEEE Access 8, 71050-71073
2020
Seeing permeability from images: fast prediction with convolutional neural networks
J Wu, X Yin, H Xiao
Science bulletin 63 (18), 1215-1222
2018
Discovery of algebraic Reynolds-stress models using sparse symbolic regression
M Schmelzer, RP Dwight, P Cinnella
Flow, Turbulence and Combustion 104, 579-603
2020
A theory-guided deep-learning formulation and optimization of seismic waveform inversion
J Sun, Z Niu, KA Innanen, J Li, DO Trad
Geophysics 85 (2), R87-R99
2020
Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned
J Wu, H Xiao, R Sun, Q Wang
Journal of Fluid Mechanics 869, 553-586
2019
A novel spatial-temporal prediction method for unsteady wake flows based on hybrid deep neural network
R Han, Y Wang, Y Zhang, G Chen
Physics of Fluids 31 (12)
2019
Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems
JL Wu, K Kashinath, A Albert, D Chirila, H Xiao
Journal of Computational Physics 406, 109209
2020
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