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

Data-driven correction reduced order models for the quasi-geostrophic equations: a numerical investigation

(2020) Changhong Mou et al.   INTERNATIONAL JOURNAL OF COMPUTATIONAL FLUID DYNAMICS

Discovering Physical Concepts with Neural Networks

(2020) Raban Iten et al.   PHYSICAL REVIEW LETTERS

Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data

(2020) Harsha Vaddireddy et al.   PHYSICS OF FLUIDS

Physics-Informed Probabilistic Learning of Linear Embeddings of Nonlinear Dynamics with Guaranteed Stability

(2020) Shaowu Pan et al.   SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS

Linearly Recurrent Autoencoder Networks for Learning Dynamics

(2019) Samuel E. Otto et al.   SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS

Deep learning and process understanding for data-driven Earth system science

(2019) Markus Reichstein et al.   NATURE

Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control

(2019) Jean Rabault et al.   JOURNAL OF FLUID MECHANICS

Digital Twin in Industry: State-of-the-Art

(2019) Fei Tao et al.   IEEE Transactions on Industrial Informatics

Multi-scale proper orthogonal decomposition of complex fluid flows

(2019) M. A. Mendez et al.   JOURNAL OF FLUID MECHANICS

Deep neural network for unsteady aerodynamic and aeroelastic modeling across multiple Mach numbers

(2019) Kai Li et al.   NONLINEAR DYNAMICS

Machine Learning for Fluid Mechanics

(2019) Steven L. Brunton et al.   Annual Review of Fluid Mechanics

The digital twin of discrete dynamic systems: Initial approaches and future challenges

(2019) R. Ganguli et al.   APPLIED MATHEMATICAL MODELLING

A domain decomposition method for the non-intrusive reduced order modelling of fluid flow

(2019) D. Xiao et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A deep learning enabler for nonintrusive reduced order modeling of fluid flows

(2019) S. Pawar et al.   PHYSICS OF FLUIDS

Modal Analysis of Fluid Flows: Applications and Outlook

(2019) Kunihiko Taira et al.   AIAA JOURNAL

An artificial neural network framework for reduced order modeling of transient flows

(2019) Omer San et al.   Communications in Nonlinear Science and Numerical Simulation

Error estimation of the parametric non-intrusive reduced order model using machine learning

(2019) D. Xiao   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

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

Perspective on machine learning for advancing fluid mechanics

(2019) M. P. Brenner et al.   Physical Review Fluids

Multi-fidelity modeling framework for nonlinear unsteady aerodynamics of airfoils

(2019) Jiaqing Kou et al.   APPLIED MATHEMATICAL MODELLING

Propagating uncertainties in large-scale hemodynamics models via network uncertainty quantification and reduced-order modeling

(2019) S. Guzzetti et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network

(2019) Zichao Long et al.   JOURNAL OF COMPUTATIONAL PHYSICS

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

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

Non-intrusive reduced-order modeling for fluid problems: A brief review

(2019) Jian Yu et al.   PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING

Neural network closures for nonlinear model order reduction

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

Reduced order modeling for nonlinear structural analysis using Gaussian process regression

(2018) Mengwu Guo et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Hidden physics models: Machine learning of nonlinear partial differential equations

(2018) Maziar Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Sparse reduced-order modelling: sensor-based dynamics to full-state estimation

(2018) Jean-Christophe Loiseau et al.   JOURNAL OF FLUID MECHANICS

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

(2018) Zhong Yi Wan et al.   PLoS One

Geometric Subspace Updates with Applications to Online Adaptive Nonlinear Model Reduction

(2018) Ralf Zimmermann et al.   SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS

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

A reduced order model for turbulent flows in the urban environment using machine learning

(2018) D. Xiao et al.   BUILDING AND ENVIRONMENT

Parameterised non-intrusive reduced order methods for ensemble Kalman filter data assimilation

(2018) D. Xiao et al.   COMPUTERS & FLUIDS

Hybrid constitutive modeling: data-driven learning of corrections to plasticity models

(2018) Rubén Ibáñez et al.   International Journal of Material Forming

pyROM: A computational framework for reduced order modeling

(2018) Vladimir Puzyrev et al.   Journal of Computational Science

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A multiscale method for model order reduction in PDE parameter estimation

(2018) Samy Wu Fung et al.   JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data

(2017) Anuj Karpatne et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Model identification of reduced order fluid dynamics systems using deep learning

(2017) Z. Wang et al.   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS

Ergodic Theory, Dynamic Mode Decomposition, and Computation of Spectral Properties of the Koopman Operator

(2017) Hassan Arbabi et al.   SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS

De-biasing the dynamic mode decomposition for applied Koopman spectral analysis of noisy datasets

(2017) Maziar S. Hemati et al.   THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS

Data-driven discovery of partial differential equations

(2017) Samuel H. Rudy et al.   Science Advances

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

(2016) Julia Ling et al.   JOURNAL OF FLUID MECHANICS

Spectral proper orthogonal decomposition

(2016) Moritz Sieber et al.   JOURNAL OF FLUID MECHANICS

Closed-Loop Turbulence Control: Progress and Challenges

(2015) Steven L. Brunton et al.   Applied Mechanics Reviews

POD/DEIM reduced-order strategies for efficient four dimensional variational data assimilation

(2015) R. Ştefănescu et al.   JOURNAL OF COMPUTATIONAL PHYSICS

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

(2015) Peter Benner et al.   SIAM REVIEW

Identification strategies for model-based control

(2013) Laurent Cordier et al.   EXPERIMENTS IN FLUIDS

AN EFFICIENT COARSE GRID PROJECTION METHOD FOR QUASIGEOSTROPHIC MODELS OF LARGE-SCALE OCEAN CIRCULATION

(2013) Omer San et al.   International Journal for Multiscale Computational Engineering

Approximate deconvolution large eddy simulation of a stratified two-layer quasigeostrophic ocean model

(2013) Omer San et al.   OCEAN MODELLING

Proper orthogonal decomposition closure models for turbulent flows: A numerical comparison

(2012) Zhu Wang et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Parametric free-form shape design with PDE models and reduced basis method

(2010) Toni Lassila et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Dynamic mode decomposition of numerical and experimental data

(2010) PETER J. SCHMID   JOURNAL OF FLUID MECHANICS

Artificial viscosity proper orthogonal decomposition

(2010) Jeff Borggaard et al.   MATHEMATICAL AND COMPUTER MODELLING

Normal forms for reduced stochastic climate models

(2009) A. J. Majda et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Distilling Free-Form Natural Laws from Experimental Data

(2009) Michael Schmidt et al.   SCIENCE

Model reduction by CPOD and Kriging

(2009) Manyu Xiao et al.   STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION

On the stability and extension of reduced-order Galerkin models in incompressible flows

(2009) Imran Akhtar et al.   THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS

Interpolation Method for Adapting Reduced-Order Models and Application to Aeroelasticity

(2008) D. Amsallem et al.   AIAA JOURNAL

Optimal control of the cylinder wake in the laminar regime by trust-region methods and POD reduced-order models

(2008) M. Bergmann et al.   JOURNAL OF COMPUTATIONAL PHYSICS