Deep learning accelerated topology optimization of linear and nonlinear structures
Year: 2020
Type: preprint
Abstract: The field of optimal design of linear elastic structures has seen manyexciting successes that resulted in new architected materials and structural
designs. With the availability of cloud computing, including high-performance
computing, machine learning, and simulation, searching for optimal nonlinear
structures is now within reach. In this study, we develop convolutional neural
network models to predict optimized designs for a given set of boundary
conditions, loads, and optimization constraints. We have considered the case of
materials with a linear elastic response with and without stress constraint.
Also, we have considered the case of materials with a hyperelastic response,
where material and geometric nonlinearities are involved. For the nonlinear
elastic case, the neo-Hookean model is utilized. For this purpose, we generate
datasets composed of the optimized designs paired with the corresponding
boundary conditions, loads, and constraints, using a topology optimization
framework to train and validate the neural network models. The developed models
are capable of accurately predicting the optimized designs without requiring an
iterative scheme and with negligible computational time. The suggested pipeline
can be generalized to other nonlinear mechanics scenarios and design domains.
Source: arXiv (Cornell University)
Authors: Diab W. Abueidda, Seid Korić, Nahil Sobh
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3170853183