Deep learning for solution and inversion of structural mechanics and vibrations
arXiv:2105.09477 · doi:10.1088/978-0-7503-3487-7ch1
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.
References in corpus (6)
- SciANN: A Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
- Variational Physics-Informed Neural Networks For Solving Partial Differential Equations
- Understanding and mitigating gradient pathologies in physics-informed neural networks
- A nonlocal physics-informed deep learning framework using the peridynamic differential operator
- Optimization for deep learning: theory and algorithms
- Machine learning acceleration of simulations of Stokesian suspensions