Titlebook: Deep Learning in Computational Mechanics; An Introductory Cour Stefan Kollmannsberger,Davide D‘Angella,Leon Herrm Textbook 2021 The Editor(

Rodent

Physics-Informed Neural Networks,e evolution in a one-dimensional spatial domain is determined using the non-linear heat equation, using both a continuous and a discrete approach. Finally, the data-driven identification is illustrated with the static bar, where the cross-sectional stiffness is estimated from the displacement.

天賦

Deep Energy Method,r to handle singularities than with the PINNs. However, this approach cannot be used for the identification of differential equations. The method is illustrated with the same static bar example from Chap.?2, where the displacement is estimated.

創(chuàng)造性

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柳樹；枯黃

大包裹

發(fā)源

Introduction,ter interest in areas other than computer science, such as physics and engineering. This chapter provides a brief overview of the recent developments in artificial intelligence. Furthermore, several ideas of different approaches using deep learning in computational mechanics are introduced. When tra

的闡明

Fundamental Concepts of Machine Learning, this using data. This chapter gives an overview of the fundamental concepts, including the data structures, learning types, and the different machine learning tasks. Additionally, the gradient descent method is introduced to illustrate how many machine learning algorithms learn through experience.

使虛弱

Neural Networks,s. ANNs serve as universal function approximators, meaning that a sufficiently complex neural network can learn almost any function in any dimension. This flexibility, combined with backpropagation and a learning algorithm, enables to learn unknown functions with an astonishing accuracy. This chapte

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