“Machine Learning Approach to Model Order Reduction of Nonlinear Systems via Autoencoder and LSTM Networks” – ETH Zürich

By | 31st August 2021

See the recent work from ETH Zürich, led by ESR Tom Simpson and carried out in collaboration with Dr. Nikos Dervilis (Uni. Sheffield), on a “Machine Learning Approach to Model Order Reduction of Nonlinear Systems via Autoencoder and LSTM Networks”.

Associate Professor of Structural Mechanics & Monitoring – Eleni Chatzi describes: “The developed scheme is inspired from the concept of nonlinear normal modes, which is here applied for the purpose of reducing costly computations for high dimensional nonlinear structural models.”

Read more in the Journal of Engineering Mechanics: 
https://lnkd.in/dkwhUsdY

https://lnkd.in/dWxg82RE

Leave a Reply

Your email address will not be published.