Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

(2020) Elizabeth Qian et al.   PHYSICA D-NONLINEAR PHENOMENA

Deep learning methods for super-resolution reconstruction of turbulent flows

(2020) Bo Liu et al.   PHYSICS OF FLUIDS

Machine learning strategies applied to the control of a fluidic pinball

(2020) C. Raibaudo et al.   PHYSICS OF FLUIDS

Assessment of supervised machine learning methods for fluid flows

(2020) Kai Fukami et al.   THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS

Koopman-Based Approach to Nonintrusive Reduced Order Modeling: Application to Aerodynamic Shape Optimization and Uncertainty Propagation

(2020) S. Ashwin Renganathan   AIAA JOURNAL

Time-series learning of latent-space dynamics for reduced-order model closure

(2020) Romit Maulik et al.   PHYSICA D-NONLINEAR PHENOMENA

Data-driven recovery of hidden physics in reduced order modeling of fluid flows

(2020) Suraj Pawar et al.   PHYSICS OF FLUIDS

Machine learning methods for turbulence modeling in subsonic flows around airfoils

(2019) Linyang Zhu et al.   PHYSICS OF FLUIDS

Space--Time Least-Squares Petrov--Galerkin Projection for Nonlinear Model Reduction

(2019) Youngsoo Choi et al.   SIAM JOURNAL ON SCIENTIFIC COMPUTING

Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem

(2019) Qian Wang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Super-resolution reconstruction of turbulent flows with machine learning

(2019) Kai Fukami et al.   JOURNAL OF FLUID MECHANICS

Fast flow field prediction over airfoils using deep learning approach

(2019) Vinothkumar Sekar et al.   PHYSICS OF FLUIDS

Active control of vortex-induced vibration of a circular cylinder using machine learning

(2019) Feng Ren et al.   PHYSICS OF FLUIDS

Accelerating deep reinforcement learning strategies of flow control through a multi-environment approach

(2019) Jean Rabault et al.   PHYSICS OF FLUIDS

Modal Analysis of Fluid Flows: Applications and Outlook

(2019) Kunihiko Taira et al.   AIAA JOURNAL

Nonlinear mode decomposition with convolutional neural networks for fluid dynamics

(2019) Takaaki Murata et al.   JOURNAL OF FLUID MECHANICS

Memory embedded non-intrusive reduced order modeling of non-ergodic flows

(2019) Shady E. Ahmed et al.   PHYSICS OF FLUIDS

Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders

(2019) Kookjin Lee et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Neural network closures for nonlinear model order reduction

(2018) Omer San et al.   ADVANCES IN COMPUTATIONAL MATHEMATICS

Data-assisted reduced-order modeling of extreme events in complex dynamical systems

(2018) Zhong Yi Wan et al.   PLoS One

Efficient collective swimming by harnessing vortices through deep reinforcement learning

(2018) Siddhartha Verma et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks

(2018) Pantelis R. Vlachas et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Generalizing Koopman Theory to Allow for Inputs and Control

(2018) Joshua L. Proctor et al.   SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS

Koopman-Based Approach to Nonintrusive Projection-Based Reduced-Order Modeling with Black-Box High-Fidelity Models

(2018) S. Ashwin Renganathan et al.   AIAA JOURNAL

Projection-based model reduction: Formulations for physics-based machine learning

(2018) Renee Swischuk et al.   COMPUTERS & FLUIDS

Physically-Constrained Data-Driven Correction for Reduced Order Modeling of Fluid Flows

(2018) M. Mohebujjaman et al.   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS

Deep learning algorithm for data-driven simulation of noisy dynamical system

(2018) Kyongmin Yeo et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Data-driven deconvolution for large eddy simulations of Kraichnan turbulence

(2018) R. Maulik et al.   PHYSICS OF FLUIDS

Deep learning for universal linear embeddings of nonlinear dynamics

(2018) Bethany Lusch et al.   Nature Communications

Dynamic Mode Decomposition with Control

(2016) Joshua L. Proctor et al.   SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS

Optimal Model Management for Multifidelity Monte Carlo Estimation

(2016) Benjamin Peherstorfer et al.   SIAM JOURNAL ON SCIENTIFIC COMPUTING

Using statistical learning to close two-fluid multiphase flow equations for a simple bubbly system

(2015) Ming Ma et al.   PHYSICS OF FLUIDS

A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems

(2015) Peter Benner et al.   SIAM REVIEW

Statistically accurate low-order models for uncertainty quantification in turbulent dynamical systems

(2013) Themistoklis P. Sapsis et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Nonlinear Model Reduction via Discrete Empirical Interpolation

(2010) Saifon Chaturantabut et al.   SIAM JOURNAL ON SCIENTIFIC COMPUTING

Universal Parametric Geometry Representation Method

(2008) B. M. Kulfan   JOURNAL OF AIRCRAFT

Stable Galerkin reduced order models for linearized compressible flow

(2008) Matthew F. Barone et al.   JOURNAL OF COMPUTATIONAL PHYSICS