An artificial neural network framework for reduced order modeling of transient flows
暂无分享,去创建一个
[1] John A. Burns,et al. Using functional gains for effective sensor location in flow control: a reduced-order modelling approach , 2015, Journal of Fluid Mechanics.
[2] Juan B. Valdés,et al. NONLINEAR MODEL FOR DROUGHT FORECASTING BASED ON A CONJUNCTION OF WAVELET TRANSFORMS AND NEURAL NETWORKS , 2003 .
[3] R. Freund. Reduced-Order Modeling Techniques Based on Krylov Subspaces and Their Use in Circuit Simulation , 1999 .
[4] J. Kutz,et al. Compressive Sensing Based Machine Learning Strategy For Characterizing The Flow Around A Cylinder With Limited Pressure Measurements , 2013 .
[5] Hugo F. S. Lui,et al. Construction of reduced-order models for fluid flows using deep feedforward neural networks , 2019, Journal of Fluid Mechanics.
[6] K. Kunisch,et al. Optimal snapshot location for computing POD basis functions , 2010 .
[7] Ahmed H. Elsheikh,et al. DR-RNN: A deep residual recurrent neural network for model reduction , 2017, ArXiv.
[8] Traian Iliescu,et al. An evolve‐then‐filter regularized reduced order model for convection‐dominated flows , 2015, 1506.07555.
[9] Eric Darve,et al. The Neural Network Approach to Inverse Problems in Differential Equations , 2019, 1901.07758.
[10] M. Yousuff Hussaini,et al. Theoretical and computational fluid dynamics , 1989 .
[11] D. A. Bistrian,et al. Randomized dynamic mode decomposition for nonintrusive reduced order modelling , 2016, 1611.04884.
[12] Brane Širok,et al. Experimental Turbulent Field Modeling by Visualization and Neural Networks , 2004 .
[13] S. Lele. Compact finite difference schemes with spectral-like resolution , 1992 .
[14] Mattan Kamon,et al. Efficient Reduced-Order Modeling of Frequency-Dependent Coupling Inductances associated with 3-D Interconnect Structures , 1995, 32nd Design Automation Conference.
[15] Zhu Wang,et al. Artificial viscosity proper orthogonal decomposition , 2011, Math. Comput. Model..
[16] M. Piggott,et al. A POD reduced order unstructured mesh ocean modelling method for moderate Reynolds number flows , 2009 .
[17] S. Michael Spottswood,et al. A review of indirect/non-intrusive reduced order modeling of nonlinear geometric structures , 2013 .
[18] Thomas A. Brenner,et al. Acceleration techniques for reduced-order models based on proper orthogonal decomposition , 2008, J. Comput. Phys..
[19] Kenneth Levenberg. A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .
[20] Bin Dong,et al. Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations , 2017, ICML.
[21] William E. Faller,et al. Unsteady fluid mechanics applications of neural networks , 1995 .
[22] Cédric Leblond,et al. Optimal flow control using a POD-based reduced-order model , 2016 .
[23] E. A. Gillies. Low-dimensional control of the circular cylinder wake , 1998, Journal of Fluid Mechanics.
[24] Nadine Aubry,et al. The dynamics of coherent structures in the wall region of a turbulent boundary layer , 1988, Journal of Fluid Mechanics.
[25] M. Samimy,et al. Modeling of subsonic cavity flows by neural networks , 2004, Proceedings of the IEEE International Conference on Mechatronics, 2004. ICM '04..
[26] Michel Loève,et al. Probability Theory I , 1977 .
[27] Eldad Haber,et al. Stable architectures for deep neural networks , 2017, ArXiv.
[28] Omer San,et al. Machine learning closures for model order reduction of thermal fluids , 2018, Applied Mathematical Modelling.
[29] Luigi Fortuna,et al. Model Order Reduction Techniques with Applications in Electrical Engineering , 1992 .
[30] C. Pain,et al. Model identification of reduced order fluid dynamics systems using deep learning , 2018 .
[31] Michael Y. Hu,et al. Forecasting with artificial neural networks: The state of the art , 1997 .
[32] Jean-Luc Aider,et al. Closed-loop separation control using machine learning , 2014, Journal of Fluid Mechanics.
[33] Laurent Cordier,et al. New Regularization Method for Calibrated POD Reduced-Order Models , 2016 .
[34] Michele Milano,et al. Application of machine learning algorithms to flow modeling and optimization , 1999 .
[35] Ming Su,et al. Leapfrogging and synoptic Leapfrogging: A new optimization approach , 2012, Comput. Chem. Eng..
[36] Ionel M. Navon,et al. An improved algorithm for the shallow water equations model reduction: Dynamic Mode Decomposition vs POD , 2015 .
[37] Rainer Storn,et al. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..
[38] L. Sirovich. Turbulence and the dynamics of coherent structures. I. Coherent structures , 1987 .
[39] Stefan Volkwein,et al. Galerkin Proper Orthogonal Decomposition Methods for a General Equation in Fluid Dynamics , 2002, SIAM J. Numer. Anal..
[40] A. Hay,et al. INTERVAL-BASED REDUCED-ORDER MODELS FOR UNSTEADY FLUID FLOW , 2007 .
[41] Christopher C. Pain,et al. A domain decomposition non-intrusive reduced order model for turbulent flows , 2019, Computers & Fluids.
[42] Qian Wang,et al. Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem , 2019, J. Comput. Phys..
[43] Petros Koumoutsakos,et al. Data-assisted reduced-order modeling of extreme events in complex dynamical systems , 2018, PloS one.
[44] C. Farhat,et al. A low‐cost, goal‐oriented ‘compact proper orthogonal decomposition’ basis for model reduction of static systems , 2011 .
[45] Ionel M. Navon,et al. Non‐intrusive reduced order modelling with least squares fitting on a sparse grid , 2017 .
[46] H. Hotelling. Analysis of a complex of statistical variables into principal components. , 1933 .
[47] Min Qi,et al. Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging , 2001, IEEE Trans. Neural Networks.
[48] Philip S. Beran,et al. Reduced-order modeling - New approaches for computational physics , 2001 .
[49] Satish Narayanan,et al. Analysis of low dimensional dynamics of flow separation , 2000 .
[50] B. R. Noack,et al. Closed-Loop Turbulence Control: Progress and Challenges , 2015 .
[51] Yin Wang,et al. Sixth order compact scheme combined with multigrid method and extrapolation technique for 2D poisson equation , 2009, J. Comput. Phys..
[52] P. Nair,et al. Nonintrusive reduced‐order modeling of parametrized time‐dependent partial differential equations , 2013 .
[53] Jose Flich,et al. Flow Control , 2011, Encyclopedia of Parallel Computing.
[54] J. Templeton. Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty , 2015 .
[55] A. Mohan,et al. A Deep Learning based Approach to Reduced Order Modeling for Turbulent Flow Control using LSTM Neural Networks , 2018, 1804.09269.
[56] J. Hesthaven,et al. Non-intrusive reduced order modeling of nonlinear problems using neural networks , 2018, J. Comput. Phys..
[57] J. Weller,et al. Numerical methods for low‐order modeling of fluid flows based on POD , 2009 .
[58] Charbel Farhat,et al. Stabilization of projection‐based reduced‐order models , 2012 .
[59] Akil C. Narayan,et al. Practical error bounds for a non-intrusive bi-fidelity approach to parametric/stochastic model reduction , 2018, J. Comput. Phys..
[60] Zhu Wang,et al. Approximate Deconvolution Reduced Order Modeling , 2015, 1510.02726.
[61] Omer San,et al. Stabilized principal interval decomposition method for model reduction of nonlinear convective systems with moving shocks , 2018, Computational and Applied Mathematics.
[62] Eric A Gillies. Low-dimensional characterization and control of non-linear wake flows , 1995 .
[63] Karen Willcox,et al. A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems , 2015, SIAM Rev..
[64] Omer San,et al. Principal interval decomposition framework for POD reduced‐order modeling of convective Boussinesq flows , 2015 .
[65] Karen Willcox,et al. Goal-oriented, model-constrained optimization for reduction of large-scale systems , 2007, J. Comput. Phys..
[66] J. Templeton,et al. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance , 2016, Journal of Fluid Mechanics.
[67] Bernard Widrow,et al. Neural networks: applications in industry, business and science , 1994, CACM.
[68] Omer San,et al. A neural network approach for the blind deconvolution of turbulent flows , 2017, Journal of Fluid Mechanics.
[69] Jean-Antoine Désidéri,et al. Stability Properties of POD–Galerkin Approximations for the Compressible Navier–Stokes Equations , 2000 .
[70] Michele Milano,et al. Neural network modeling for near wall turbulent flow , 2002 .
[71] R.A. Sahan,et al. Artificial neural network-based modeling and intelligent control of transitional flows , 1997, Proceedings of the 1997 IEEE International Conference on Control Applications.
[72] Marco Debiasi,et al. Control of Subsonic Cavity Flows by Neural Networks - Analytical Models and Experimental Validation , 2005 .
[73] Billie F. Spencer,et al. Modeling and Control of Magnetorheological Dampers for Seismic Response Reduction , 1996 .
[74] R. Rico-Martinez,et al. Low-dimensional models for active control of flow separation , 1999, Proceedings of the 1999 IEEE International Conference on Control Applications (Cat. No.99CH36328).
[75] Jaijeet Roychowdhury,et al. Reduced-order modeling of time-varying systems , 1999 .
[76] Zhu Wang,et al. Two-level discretizations of nonlinear closure models for proper orthogonal decomposition , 2011, J. Comput. Phys..
[77] Julia Ling,et al. Machine learning strategies for systems with invariance properties , 2016, J. Comput. Phys..
[78] Bernd R. Noack,et al. Identification strategies for model-based control , 2013 .
[79] C. Pain,et al. Non‐intrusive reduced‐order modelling of the Navier–Stokes equations based on RBF interpolation , 2015 .
[80] Cheng Wang,et al. Analysis of finite difference schemes for unsteady Navier-Stokes equations in vorticity formulation , 2002, Numerische Mathematik.
[81] S. Ravindran. A reduced-order approach for optimal control of fluids using proper orthogonal decomposition , 2000 .
[82] Traian Iliescu,et al. A stabilized proper orthogonal decomposition reduced-order model for large scale quasigeostrophic ocean circulation , 2014, Adv. Comput. Math..
[83] B. R. Noack,et al. On the need for a nonlinear subscale turbulence term in POD models as exemplified for a high-Reynolds-number flow over an Ahmed body , 2013, Journal of Fluid Mechanics.
[84] Maria Vittoria Salvetti,et al. Low-dimensional modelling of a confined three-dimensional wake flow , 2006, Journal of Fluid Mechanics.
[85] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[86] E. A. Gillies. Multiple sensor control of vortex shedding , 2001 .
[87] Traian Iliescu,et al. Energy balance and mass conservation in reduced order models of fluid flows , 2017, J. Comput. Phys..
[88] Yuji Hattori,et al. Searching for turbulence models by artificial neural network , 2016, 1607.01042.
[89] Christian W. Dawson,et al. An artificial neural network approach to rainfall-runoff modelling , 1998 .
[90] Sirod Sirisup,et al. On-line and Off-line POD Assisted Projective Integral for Non-linear Problems: A Case Study with Burgers-Equation , 2011 .
[91] Simon Haykin,et al. Neural Networks and Learning Machines , 2010 .
[92] Martin T. Hagan,et al. Neural network design , 1995 .
[93] B. R. Noack. Turbulence, Coherent Structures, Dynamical Systems and Symmetry , 2013 .
[94] Ionel M. Navon,et al. Non-intrusive reduced order modelling of the Navier-Stokes equations , 2015 .
[95] David Duvenaud,et al. Neural Ordinary Differential Equations , 2018, NeurIPS.
[96] P. Holmes,et al. The Proper Orthogonal Decomposition in the Analysis of Turbulent Flows , 1993 .
[97] Omer San,et al. Extreme learning machine for reduced order modeling of turbulent geophysical flows. , 2018, Physical review. E.
[98] Petros Boufounos,et al. Sparse Sensing and DMD-Based Identification of Flow Regimes and Bifurcations in Complex Flows , 2015, SIAM J. Appl. Dyn. Syst..
[99] Gilead Tadmor,et al. Reduced-Order Modelling for Flow Control , 2013 .
[100] Omer San,et al. Neural network closures for nonlinear model order reduction , 2017, Adv. Comput. Math..
[101] Scott T. M. Dawson,et al. Model Reduction for Flow Analysis and Control , 2017 .
[102] Claude Brezinski,et al. Numerical Methods for Engineers and Scientists , 1992 .
[103] G. Kerschen,et al. The Method of Proper Orthogonal Decomposition for Dynamical Characterization and Order Reduction of Mechanical Systems: An Overview , 2005 .
[104] Traian Iliescu,et al. Proper orthogonal decomposition closure models for fluid flows: Burgers equation , 2013, 1308.3276.
[105] K. Willcox,et al. Data-driven operator inference for nonintrusive projection-based model reduction , 2016 .
[106] M. Loève. Probability theory : foundations, random sequences , 1955 .
[107] Russell C. Eberhart,et al. A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.
[108] Chi-Wang Shu,et al. Total variation diminishing Runge-Kutta schemes , 1998, Math. Comput..
[109] Traian Iliescu,et al. Data-Driven Filtered Reduced Order Modeling of Fluid Flows , 2017, SIAM J. Sci. Comput..
[110] D. Gottlieb,et al. Stable and accurate boundary treatments for compact, high-order finite-difference schemes , 1993 .
[111] R. Goodman,et al. Application of neural networks to turbulence control for drag reduction , 1997 .
[112] Brendan D. Tracey,et al. Application of supervised learning to quantify uncertainties in turbulence and combustion modeling , 2013 .
[113] J. Hesthaven,et al. Greedy Nonintrusive Reduced Order Model for Fluid Dynamics , 2018, AIAA Journal.
[114] Julia Kluge. Applied And Computational Control Signals And Circuits , 2016 .
[115] Traian Iliescu,et al. Proper orthogonal decomposition closure models for turbulent flows: A numerical comparison , 2011, 1106.3585.
[116] K. Duraisamy,et al. Using field inversion to quantify functional errors in turbulence closures , 2016 .
[117] Vassilios Theofilis,et al. Modal Analysis of Fluid Flows: An Overview , 2017, 1702.01453.
[118] H. Tran,et al. Modeling and control of physical processes using proper orthogonal decomposition , 2001 .
[119] Adrian Sandu,et al. Efficient Construction of Local Parametric Reduced Order Models Using Machine Learning Techniques , 2015, ArXiv.
[120] Zhu Wang,et al. Numerical analysis of the Leray reduced order model , 2017, J. Comput. Appl. Math..