Deep multimodal autoencoder for crack criticality assessment

DOI10.1002/nme.6905MaRDI QIDQ6089256

François Willot, Unnamed Author, Unnamed Author, Unnamed Author, Jacques Besson, David Ryckelynck

Publication date: 17 November 2023

Published in: International Journal for Numerical Methods in Engineering (Search for Journal in Brave)

zbMATH Keywords

finite element methodtransfer learningdigital twinCNNmultimodal autoencoder

Mathematics Subject Classification ID

Plastic materials, materials of stress-rate and internal-variable type (74Cxx) Material properties given special treatment (74Exx)

Cites Work

• Unnamed Item
• Unnamed Item
• Precise Hausdorff distance computation between polygonal meshes
• Large scale object-oriented finite element code design
• Polymorphic constitutive equations in finite element codes
• Anisotropic additive plasticity in the logarithmic strain space: Modular kinematic formulation and implementation based on incremental minimization principles for standard materials.
• Non-convergence of stochastic gradient descent in the training of deep neural networks
• Multidimensional scaling and visualization of patterns in distribution of nontrivial zeros of the zeta-function
• A machine learning based plasticity model using proper orthogonal decomposition
• Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
• Modern multidimensional scaling. Theory and applications.
• Object-Oriented Programming Applied to the Finite Element Method Part I. General Concepts
• Object-Oriented Programming Applied to the Finite Element Method Part II. Application to Material Behaviors
• Reducing the Dimensionality of Data with Neural Networks

This page was built for publication: Deep multimodal autoencoder for crack criticality assessment