# Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning

@article{Carlberg2018RecoveringMC, title={Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning}, author={Kevin T. Carlberg and Antony Jameson and Mykel J. Kochenderfer and Jeremy Morton and Liqian Peng and Freddie D. Witherden}, journal={ArXiv}, year={2018}, volume={abs/1812.01177} }

## 42 Citations

### Enabling Nonlinear Manifold Projection Reduced-Order Models by Extending Convolutional Neural Networks to Unstructured Data

- Computer ScienceArXiv
- 2020

A nonlinear manifold learning technique based on deep autoencoders that is appropriate for model order reduction of physical systems in complex geometries and shows better than an order of magnitude improvement in accuracy over linear subspace methods.

### A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data Using Unstructured Spatial Discretizations

- Computer ScienceSIAM J. Sci. Comput.
- 2021

A nonlinear manifold learning technique based on deep convolutional autoencoders that is appropriate for model order reduction of physical systems in complex geometries and shows better than an order of magnitude improvement in accuracy over linear methods.

### Assessment of unsteady flow predictions using hybrid deep learning based reduced-order models

- Computer Science
- 2020

Two deep learning-based hybrid data-driven reduced order models for the prediction of unsteady fluid flows are presented, and an observer-corrector method for the calculation of integrated pressure force coefficients on the fluid-solid boundary on a reference grid is introduced.

### Machine learning for fluid flow reconstruction from limited measurements

- Computer ScienceJ. Comput. Phys.
- 2022

### Shallow Learning for Fluid Flow Reconstruction with Limited Sensors and Limited Data

- Computer ScienceArXiv
- 2019

This work proposes a shallow neural network-based learning methodology for fluid flow reconstruction that learns an end-to-end mapping between the sensor measurements and the high-dimensional fluid flow field, without any heavy preprocessing on the raw data.

### Non-intrusive reduced-order modeling using uncertainty-aware Deep Neural Networks and Proper Orthogonal Decomposition: Application to flood modeling

- Computer ScienceJ. Comput. Phys.
- 2021

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

- Computer ScienceJ. Comput. Phys.
- 2020

### Shallow neural networks for fluid flow reconstruction with limited sensors

- Computer ScienceProceedings of the Royal Society A
- 2020

This work proposes a shallow neural network-based learning methodology for fluid flow reconstruction that learns an end-to-end mapping between the sensor measurements and the high-dimensional fluid flow field, without any heavy preprocessing on the raw data.

### Non-intrusive nonlinear model reduction via machine learning approximations to low-dimensional operators

- Computer ScienceAdv. Model. Simul. Eng. Sci.
- 2021

This work proposes a method that enables traditionally intrusive reduced-order models to be accurately approximated in a non-intrusive manner using modern machine-learning regression techniques, and demonstrates the effectiveness of the proposed technique on two types of PDEs.

### Non-intrusive Nonlinear Model Reduction via Machine Learning Approximations to Low-dimensional Operators

- Computer Science
- 2021

This work proposes a method that enables traditionally intrusive reduced-order models to be accurately approximated in a non-intrusive man-ner, and demonstrates the effectiveness of the proposed technique on two types of PDEs.

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