相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。PDE-constrained models with neural network terms: Optimization and global convergence
Justin Sirignano et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2023)
Comparative analysis of machine learning methods for active flow control
Fabio Pino et al.
JOURNAL OF FLUID MECHANICS (2023)
Stable a posteriori LES of 2D turbulence using convolutional neural networks: Backscattering analysis and generalization to higher Re via transfer learning
Yifei Guan et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2022)
Scientific multi-agent reinforcement learning for wall-models of turbulent flows
H. Jane Bae et al.
NATURE COMMUNICATIONS (2022)
Efficient assimilation of sparse data into RANS-based turbulent flow simulations using a discrete adjoint method
Oliver Brenner et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2022)
Embedded training of neural-network subgrid-scale turbulence models
Jonathan F. MacArt et al.
PHYSICAL REVIEW FLUIDS (2021)
Large eddy simulation of aircraft at affordable cost: a milestone in computational fluid dynamics
Konrad A. Goc et al.
FLOW (2021)
Machine Learning for Fluid Mechanics
Steven L. Brunton et al.
ANNUAL REVIEW OF FLUID MECHANICS, VOL 52 (2020)
DPM: A deep learning PDE augmentation method with application to large-eddy simulation
Justin Sirignano et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2020)
Turbulence Modeling in the Age of Data
Karthik Duraisamy et al.
ANNUAL REVIEW OF FLUID MECHANICS, VOL 51 (2019)
Anisotropic minimum-dissipation (AMD) subgrid-scale model implemented in OpenFOAM: Verification and assessment in single-phase and multi-phase flows
Amir-Pouyan Zahiri et al.
COMPUTERS & FLUIDS (2019)
Deep neural networks for data-driven LES closure models
Andrea Beck et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2019)
Wall-Modeled Large-Eddy Simulation for Complex Turbulent Flows
Sanjeeb T. Bose et al.
ANNUAL REVIEW OF FLUID MECHANICS, VOL 50 (2018)
A data assimilation model for turbulent flows using continuous adjoint formulation
Chuangxin He et al.
PHYSICS OF FLUIDS (2018)
On the role and challenges of CFD in the aerospace industry
P. R. Spalart et al.
AERONAUTICAL JOURNAL (2016)
Machine learning strategies for systems with invariance properties
Julia Ling et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2016)
A paradigm for data-driven predictive modeling using field inversion and machine learning
Eric J. Parish et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2016)
Semi-implicit iterative methods for low Mach number turbulent reacting flows: Operator splitting versus approximate factorization
Jonathan F. MacArt et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2016)
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
Julia Ling et al.
JOURNAL OF FLUID MECHANICS (2016)
Minimum-dissipation models for large-eddy simulation
Wybe Rozema et al.
PHYSICS OF FLUIDS (2015)
High order conservative finite difference scheme for variable density low Mach number turbulent flows
Olivier Desjardins et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2008)
Overview of global data assimilation developments in numerical weather-prediction centres
Florence Rabier
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY (2005)