Deep learning operator network for plastic deformation with variable loads and material properties

The advent of data-driven and physics-informed neural networks has sparked interest in deep neural networks as universal approximators of solutions in various scientific and engineering communities. However, in most existing approaches, neural networks can only provide solutions for a fixed set of i...

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Bibliographic Details
Published in:	Engineering with computers Vol. 40; no. 2; pp. 917 - 929
Main Authors:	Koric, Seid, Viswantah, Asha, Abueidda, Diab W., Sobh, Nahil A., Khan, Kamran
Format:	Journal Article
Language:	English
Published:	London Springer London 01.04.2024 Springer Nature B.V
Subjects:	Artificial neural networks Boundary conditions CAE) and Design Calculus of Variations and Optimal Control; Optimization Classical Mechanics Computer Science Computer-Aided Engineering (CAD Control Deep learning Deformation Design optimization Initial conditions Machine learning Material properties Math. Applications in Chemistry Mathematical and Computational Engineering Neural networks Numerical methods Operators (mathematics) Original Article Parameter sensitivity Parametric analysis Plastic deformation Sensitivity analysis Stress distribution Systems Theory Deep learning Material nonlinearity Deep operator network (DeepONet) Machine learning Plasticity
ISSN:	0177-0667, 1435-5663
Online Access:	Get full text
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