Abstract
Significant advancements have been made in the field of artificial intelligence in recent years. Thus, artificial intelligence has also become of greater 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 transferring the artificial intelligence approaches from computer science to physics and engineering, the main obstacle is the lack of data. This difficulty is overcome by enforcing the underlying physics in the learning algorithms. Finally, the chapter presents the outline of the book to orientate the reader.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Stuart J. Russell, Peter Norvig, and Ernest Davis. Artificial intelligence: a modern approach. 3rd ed. Prentice Hall series in artificial intelligence. Upper Saddle River: Prentice Hall, 2010. 1132 pp. ISBN: 978-0-13-604259-4.
Philippe Lambin et al. “Radiomics: Extracting more information from medical images using advanced feature analysis”. In: European Journal of Cancer 48.4 (Mar. 2012), pp. 441–446. ISSN: 09598049. DOI https://doi.org/10.1016/j.ejca.2011.11.036. URL: https://linkinghub.elsevier.com/retrieve/pii/S0959804911009993 (visited on 07/02/2020).
M. Raissi, P. Perdikaris, and G.E. Karniadakis. “Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial di erential equations”. In: Journal of Computational Physics 378 (Feb. 2019), pp. 686–707. ISSN: 00219991. DOI https://doi.org/10.1016/j.jcp.2018.10.045. URL: https://linkinghub.elsevier.com/retrieve/pii/S0021999118307125 (visited on 01/08/2020).
Esteban Samaniego et al. “An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications”. In: arXiv:1908.10407 [cs, math, stat] (Sept. 2, 2019). URL: http://arxiv.org/abs/1908.10407 (visited on 01/08/2020).
Mohammad Amin Nabian and Hadi Meidani. “A Deep Neural Network Surrogate for High-Dimensional Random Partial Differential Equations”. In: Probabilistic Engineering Mechanics 57 (July 2019), pp. 14–25. ISSN: 02668920. https://doi.org/10.1016/j.probengmech.2019.05.001. URL: http://arxiv.org/abs/1806.02957 (visited on 02/21/2020).
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. ISBN: 0-262-03561-8. URL: http://www.deeplearningbook.org.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Kollmannsberger, S., D’Angella, D., Jokeit, M., Herrmann, L. (2021). Introduction. In: Deep Learning in Computational Mechanics. Studies in Computational Intelligence, vol 977. Springer, Cham. https://doi.org/10.1007/978-3-030-76587-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-76587-3_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-76586-6
Online ISBN: 978-3-030-76587-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)