Deep Learning in Computational Mechanics

DOI10.1007/978-3-030-76587-3zbMath1470.68005OpenAlexW4252091197MaRDI QIDQ4995007

Moritz Jokeit, Leon Herrmann, Davide D'Angella, Stefan Kollmannsberger

Publication date: 22 June 2021

Published in: Studies in Computational Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-76587-3

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Introductory exposition (textbooks, tutorial papers, etc.) pertaining to computer science (68-01) Learning and adaptive systems in artificial intelligence (68T05) Computational methods for problems pertaining to mechanics of particles and systems (70-08) Physics (00A79) Numerical and other methods in solid mechanics (74S99)

Related Items (10)

Physics informed neural networks for continuum micromechanics ⋮ Scientific machine learning through physics-informed neural networks: where we are and what's next ⋮ A surrogate model for the prediction of permeabilities and flow through porous media: a machine learning approach based on stochastic Brownian motion ⋮ A comparative study on different neural network architectures to model inelasticity ⋮ On the use of neural networks for full waveform inversion ⋮ Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteria ⋮ \(r\)-adaptive deep learning method for solving partial differential equations ⋮ Optimization of physics-informed neural networks for solving the nolinear Schrödinger equation ⋮ Finite electro-elasticity with physics-augmented neural networks ⋮ A physics-informed learning approach to Bernoulli-type free boundary problems

Uses Software

• PyTorch

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