Reduced Order Modeling Based on Neural Networks

Model order reduction assisted by deep neural networks (ROM-net)

The cost of numerical simulations can be reduced by projection-based model order reduction, which consists in restricting the search of the ...

Tutorial: Model order reduction with artificial neural networks

Recent success of artificial neural networks led to the development of several methods for model order reduction using neural networks. pyMOR provides the ...

Reduced Order Modeling Based on Neural Networks - SciEngineer

Reduced Order Modeling (ROM) can help engineers and scientists to reduce the computation required without compromising the accuracy of the ...

Convolutional neural network based reduced order modeling for ...

In this paper, we combine convolutional neural networks (CNNs) with reduced order modeling (ROM) for efficient simulations of multiscale problems.

Physics-informed machine learning for reduced-order modeling of ...

A reduced basis method based on a physics-informed machine learning framework is developed for efficient reduced-order modeling of parametrized partial ...

Using an Artificial Neural Network for Reduced Order Modeling

Artificial neural networks can be used to build a reduced order model of a piece of equipment, such as a methanol synthesis reactor. The model can then be ...

Compressed neural networks for reduced order modeling

Reduced order modeling (ROM) techniques, such as proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD), ...

Deep Learning for Reduced Order Modelling and Efficient Temporal ...

Abstract:Reduced Order Modelling (ROM) has been widely used to create lower order, computationally inexpensive representations of ...

Artificial neural network based correction for reduced order models ...

The main idea is to start from a purely POD-based ROM, projecting the equations onto the ROM space, and then add a nonlinear correction that depends on the ROM ...

Reduced Order Modeling - MATLAB & Simulink - MathWorks

Reduced order modeling (ROM) and model order reduction (MOR) are techniques for reducing the computational complexity of a full-order, high-fidelity model.

Reduced Order Modeling based on Neural Networks | Lorant Szabo

In this video we will explain how to use Machine Learning for Run-Time Optimization and build an LSTM-ROM model (a type of ROM that ...

Deep learning‐based reduced order models for the real‐time ...

We propose a non-intrusive deep learning-based reduced order model (DL-ROM) capable of capturing the complex dynamics of mechanical systems ...

Model Order Reduction with Neural Networks: Application to ...

We investigate the capability of neural network-based model order reduction, ie, autoencoder (AE), for fluid flows.

Deep learning for reduced order modelling and efficient temporal ...

In this work, we develop a novel deep learning framework DL-ROM (deep learning—reduced order modeling) to create a neural network capable of non ...

Neural network-based reduced-order modeling for nonlinear vertical ...

Abstract. In this paper, a nonlinear reduced-order model based on neural networks is introduced in order to model vertical sloshing in presence ...

AI with Model-Based Design: Reduced Order Modeling - MathWorks

Dynamic methods include deep learning methods such as Long Short Term Memory networks or LSTM networks, neural state space or neural ODEs, or ...

An artificial neural network framework for reduced order modeling of ...

Our approach utilizes a training process from full-order scale direct numerical simulation data projected on proper orthogonal decomposition (POD) modes to ...

Model order reduction assisted by deep neural networks (ROM-net)

In this talk from June 10, 2021, David Ryckelynck of MINES ParisTech University discusses a general framework for projection-based model ...

Using AI for Reduced-Order Modeling - MATLAB Central Blogs

Using Neural State-Space Models, introduced in MATLAB R2022b, you can create a deep learning-based non-linear state-space model using ...

[PDF] Model order reduction based on Runge–Kutta neural networks

Model Order Reduction methods enable the generation of real-time-capable digital twins, which can enable various novel value streams in industry and ...