Discovering causal relations and equations from data
Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …
questions about why natural phenomena occur and to make testable models that explain the …
Data-driven modeling for unsteady aerodynamics and aeroelasticity
Aerodynamic modeling plays an important role in multiphysics and design problems, in
addition to experiment and numerical simulation, due to its low-dimensional representation …
addition to experiment and numerical simulation, due to its low-dimensional representation …
[图书][B] Data-driven science and engineering: Machine learning, dynamical systems, and control
SL Brunton, JN Kutz - 2022 - books.google.com
Data-driven discovery is revolutionizing how we model, predict, and control complex
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …
[HTML][HTML] Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders
A common strategy for the dimensionality reduction of nonlinear partial differential equations
(PDEs) relies on the use of the proper orthogonal decomposition (POD) to identify a reduced …
(PDEs) relies on the use of the proper orthogonal decomposition (POD) to identify a reduced …
Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics
J Xu, K Duraisamy - Computer Methods in Applied Mechanics and …, 2020 - Elsevier
A data-driven framework is proposed towards the end of predictive modeling of complex
spatio-temporal dynamics, leveraging nested non-linear manifolds. Three levels of neural …
spatio-temporal dynamics, leveraging nested non-linear manifolds. Three levels of neural …
Data-driven POD-Galerkin reduced order model for turbulent flows
In this work we present a Reduced Order Model which is specifically designed to deal with
turbulent flows in a finite volume setting. The method used to build the reduced order model …
turbulent flows in a finite volume setting. The method used to build the reduced order model …
Model identification of reduced order fluid dynamics systems using deep learning
This paper presents a novel model reduction method: deep learning reduced order model,
which is based on proper orthogonal decomposition and deep learning methods. The deep …
which is based on proper orthogonal decomposition and deep learning methods. The deep …
A deep learning enabler for nonintrusive reduced order modeling of fluid flows
In this paper, we introduce a modular deep neural network (DNN) framework for data-driven
reduced order modeling of dynamical systems relevant to fluid flows. We propose various …
reduced order modeling of dynamical systems relevant to fluid flows. We propose various …
Galerkin v. least-squares Petrov–Galerkin projection in nonlinear model reduction
Abstract Least-squares Petrov–Galerkin (LSPG) model-reduction techniques such as the
Gauss–Newton with Approximated Tensors (GNAT) method have shown promise, as they …
Gauss–Newton with Approximated Tensors (GNAT) method have shown promise, as they …
Supremizer stabilization of POD–Galerkin approximation of parametrized steady incompressible Navier–Stokes equations
Supremizer stabilization of POD–Galerkin approximation of parametrized steady
incompressible Navier–Stokes equations - Ballarin - 2015 - International Journal for Numerical …
incompressible Navier–Stokes equations - Ballarin - 2015 - International Journal for Numerical …