The second edition of the textbook “Deep Learning in Computational Mechanics” is now published online at https://link.springer.com/book/10.1007/978-3-031-89529-6! It will also be available as print from December 26.

Prof. Oliver Weeger joined as new co-author for this edition, but the major acknowledgment and thanks goes to Prof. Stefan Kollmannsberger, Leon Herrmann and Moritz Jokeit for their fantastic work!

Compared to the first edition, this revised version was substantially expanded and covers many additional topics! It provides a first course on machine learning in computational mechanics without requiring specific prerequisite knowledge.

Fundamental concepts of machine learning are introduced before explaining neural networks. With this knowledge, prominent topics in deep learning for simulation are explored. These include surrogate modeling, physics-informed neural networks, generative artificial intelligence, Hamiltonian and Lagrangian neural networks, input convex neural networks, and more general machine learning techniques.

The idea of the book is to provide basic concepts as simple as possible but in a mathematically sound manner. Starting point are one-dimensional examples including elasticity, plasticity, heat evolution, or wave propagation. The concepts are then expanded to state-of-the-art applications in material modeling, generative artificial intelligence, topology optimization, defect detection, and inverse problems.

Codes and further accompanying materials are also available at:
http://deeplearningincomputationalmechanics.com