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20172022
most citedMachine Learning Approach to Model Order Reduction of Nonlinear Systems via Autoencoder and LSTM Networks

68 citations · 172 across the 9 of their papers we have counts for

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Showing cs.CEShow all

5 papers · 1 filter

cs.CE2022

Nonlinear Reduced Order Modelling of Soil Structure Interaction Effects via LSTM and Autoencoder Neural Networks

Thomas Simpson, Nikolaos Dervilis, Philippe Couturier +2

In the field of structural health monitoring (SHM), inverse problems which require repeated analyses are common. With the increase in the use of nonlinear models, the development o…

cs.CE202168 cited

Machine Learning Approach to Model Order Reduction of Nonlinear Systems via Autoencoder and LSTM Networks

Thomas Simpson, Nikolaos Dervilis, Eleni Chatzi

In analyzing and assessing the condition of dynamical systems, it is necessary to account for nonlinearity. Recent advances in computation have rendered previously computationally…

cs.CE20211 cited

Relational VAE: A Continuous Latent Variable Model for Graph Structured Data

Charilaos Mylonas, Imad Abdallah, Eleni Chatzi

Graph Networks (GNs) enable the fusion of prior knowledge and relational reasoning with flexible function approximations. In this work, a general GN-based model is proposed which t…

cs.CE2020

On Dynamic Substructuring of Systems with Localised Nonlinearities

Thomas Simpson, Dimitrios Giagopoulos, Vasilis Dertimanis +1

Dynamic substructuring (DS) methods encompass a range of techniques to decompose large structural systems into multiple coupled subsystems. This decomposition has the principle ben…

cs.CE2017

Multiscale Surrogate Modeling and Uncertainty Quantification for Periodic Composite Structures

Charilaos Mylonas, Valentin Bemetz, Eleni Chatzi

Computational modeling of the structural behavior of continuous fiber composite materials often takes into account the periodicity of the underlying micro-structure. A well establi…