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] Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
High-resolution (HR) information of fluid flows, although preferable, is usually less
accessible due to limited computational or experimental resources. In many cases, fluid data …
accessible due to limited computational or experimental resources. In many cases, fluid data …
Super-resolution generative adversarial networks of randomly-seeded fields
Reconstruction of field quantities from sparse measurements is a problem arising in a broad
spectrum of applications. This task is particularly challenging when the mapping between …
spectrum of applications. This task is particularly challenging when the mapping between …
[HTML][HTML] Physics-constrained bayesian neural network for fluid flow reconstruction with sparse and noisy data
In many applications, flow measurements are usually sparse and possibly noisy. The
reconstruction of a high-resolution flow field from limited and imperfect flow information is …
reconstruction of a high-resolution flow field from limited and imperfect flow information is …
Efficient non‐linear model reduction via a least‐squares Petrov–Galerkin projection and compressive tensor approximations
K Carlberg, C Bou‐Mosleh… - International Journal for …, 2011 - Wiley Online Library
A Petrov–Galerkin projection method is proposed for reducing the dimension of a discrete
non‐linear static or dynamic computational model in view of enabling its processing in real …
non‐linear static or dynamic computational model in view of enabling its processing in real …
Structure‐preserving, stability, and accuracy properties of the energy‐conserving sampling and weighting method for the hyper reduction of nonlinear finite element …
The computational efficiency of a typical, projection‐based, nonlinear model reduction
method hinges on the efficient approximation, for explicit computations, of the scalar …
method hinges on the efficient approximation, for explicit computations, of the scalar …
Robust flow reconstruction from limited measurements via sparse representation
In many applications it is important to estimate a fluid flow field from limited and possibly
corrupt measurements. Current methods in flow estimation often use least squares …
corrupt measurements. Current methods in flow estimation often use least squares …
Missing point estimation in models described by proper orthogonal decomposition
P Astrid, S Weiland, K Willcox… - IEEE Transactions on …, 2008 - ieeexplore.ieee.org
This paper presents a new method of missing point estimation (MPE) to derive efficient
reduced-order models for large-scale parameter-varying systems. Such systems often result …
reduced-order models for large-scale parameter-varying systems. Such systems often result …
Multi-scale proper orthogonal decomposition of complex fluid flows
MA Mendez, M Balabane, JM Buchlin - Journal of Fluid Mechanics, 2019 - cambridge.org
Data-driven decompositions are becoming essential tools in fluid dynamics, allowing for
tracking the evolution of coherent patterns in large datasets, and for constructing low-order …
tracking the evolution of coherent patterns in large datasets, and for constructing low-order …