# A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder

@article{Kim2022AFA, title={A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder}, author={Youngkyu Kim and Youngsoo Choi and David Peter Widemann and Tarek Zohdi}, journal={J. Comput. Phys.}, year={2022}, volume={451}, pages={110841} }

## 61 Citations

### Reduced Order Modeling Using Advection-Aware Autoencoders

- Computer ScienceMathematical and Computational Applications
- 2022

An advection-aware (AA) autoencoder network that can address some of these limitations by learning efficient, physics-informed, nonlinear embeddings of the high-fidelity system snapshots is developed.

### Data-driven reduced order modeling of environmental hydrodynamics using deep autoencoders and neural ODEs

- Computer Science9th edition of the International Conference on Computational Methods for Coupled Problems in Science and Engineering
- 2021

Findings indicate that deep autoencoders can leverage nonlinear manifold learning to achieve a highly efficient compression of spatial information and define a latent-space that appears to be more suitable for capturing the temporal dynamics through the NODE framework.

### Non-linear manifold ROM with Convolutional Autoencoders and Reduced Over-Collocation method

- Computer ScienceArXiv
- 2022

The non-linear manifold method introduced by Carlberg et al is implemented with hyper-reduction achieved through reduced overcollocation and teacher-student training of a reduced decoder, and the results obtained are compared with a purely data-driven method for which the dynamics is evolved in time with a long-short term memory network.

### Reduced-order modeling for stochastic large-scale and time-dependent problems using deep spatial and temporal convolutional autoencoders

- Computer Science
- 2022

The numerical results show that the proposed nonlinear framework presents strong predictive abilities to accurately approximate the statistical moments of the outputs for complex stochastic large-scale and time-dependent problems, with low computational cost during the predictive online stage.

### A learning-based projection method for model order reduction of transport problems

- Computer ScienceJ. Comput. Appl. Math.
- 2023

### POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition

- Computer ScienceComputer Methods in Applied Mechanics and Engineering
- 2022

### Three-dimensional deep learning-based reduced order model for unsteady flow dynamics with variable Reynolds number

- Computer SciencePhysics of Fluids
- 2022

The proposed DL-ROM framework aligns with the development of a digital twin for 3D unsteady flow field and instantaneous force predictions with variable Re-based effects and an assessment of the computing requirements in terms of the memory usage, training, and testing cost of the 3D CRAN framework.

### Adaptive Physics-Informed Neural Operator for Coarse-Grained Non-Equilibrium Flows

- Computer ScienceArXiv
- 2022

This work lays the foundation for constructing an efﬁcient ML-based surrogate coupled with reactive Navier-Stokes solvers for accurately characterizing non-equilibrium phenomena.

### Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning

- Computer Science
- 2022

BT-AE framework, a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches, relies on the combination of an autoencoder and Barlow Twins self-supervised learning, where BT maximizes the information content of the embedding with the latent space through a joint embedding architecture.

### Reduced order modeling with Barlow Twins self-supervised learning: Navigating the space between linear and nonlinear solution manifolds

- Computer ScienceArXiv
- 2022

BT-AE framework, a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches, relies on the combination of an autoencoder and Barlow Twins self-supervised learning, where BT maximizes the information content of the embedding with the latent space through a joint embedding architecture.

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