Deep learning for solution and inversion of structural mechanics and vibrations

TL;DR

This paper reviews the application of deep learning and physics-informed neural networks to structural mechanics and vibrations, focusing on data de-noising, solving differential equations, and system response characterization.

Contribution

It introduces recent deep learning techniques tailored for structural mechanics and vibration analysis, emphasizing physics-informed neural networks.

Findings

Effective data de-noising methods demonstrated

Successful solutions to time-dependent differential equations

Enhanced system response characterization achieved

Abstract

Deep learning has been the most popular machine learning method in the last few years. In this chapter, we present the application of deep learning and physics-informed neural networks concerning structural mechanics and vibration problems. Demonstration problems involve de-noising data, solution to time-dependent ordinary and partial differential equations, and characterizing the system's response for a given data.