Background information of deep learning for structural engineering

DOI10.1007/s11831-017-9237-0zbMath1390.68538OpenAlexW2729750142WikidataQ113322982 ScholiaQ113322982MaRDI QIDQ1639595

Seunghye Lee, Jaehong Lee, Jingwan Ha, Mehriniso Zokhirova, Hyeonjoon Moon

Publication date: 13 June 2018

Published in: Archives of Computational Methods in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11831-017-9237-0

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Optimization problems in solid mechanics (74P99)

Related Items (7)

Müntz-Legendre neural network construction for solving delay optimal control problems of fractional order with equality and inequality constraints ⋮ Deep learned one‐iteration nonlinear solver for solid mechanics ⋮ Optimization Design of Laminated Functionally Carbon Nanotube-Reinforced Composite Plates Using Deep Neural Networks and Differential Evolution ⋮ A novel analysis-prediction approach for geometrically nonlinear problems using group method of data handling ⋮ Machine learning-combined topology optimization for functionary graded composite structure design ⋮ A deep learning approach for efficiently and accurately evaluating the flow field of supercritical airfoils ⋮ A robust unsupervised neural network framework for geometrically nonlinear analysis of inelastic truss structures

Uses Software

• Theano
• darch
• Keras
• AdaGrad
• RMSprop
• Adam
• GitHub
• ADADELTA

Cites Work

• Neurobiological computational models in structural analysis and design
• A Fast Learning Algorithm for Deep Belief Nets
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