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Ling Wu

University of Liège

Leige

Belgium

  • University of Liège

SCHOLARLY PAPERS

6

DOWNLOADS

360

TOTAL CITATIONS

3

Scholarly Papers (6)

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1.

Stochastic Deep Material Networks as Efficient Surrogates for Stochastic Homogenisation of Non-Linear Heterogeneous Materials

Number of pages: 44 Posted: 14 Feb 2025
Ling Wu and Ludovic Noels
University of Liège and University of Liège
Downloads 87 (765,566)
Citation 3

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Deep-Material Network, Stochastic, Composites, Stochastic Volume Elements (SVEs), Homogenisation

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Artificial Neural Network, Recurrent Neural Network, Self-consistency, Surrogate, Multi-scale, Elasto-plasticity

3.

Convolutional Neural Network-Based Mapping of Material Micro-Structures to Deep Material Networks for Non-Linear Mechanical Response Prediction

Number of pages: 40 Posted: 10 Jul 2025
Ling Wu and Ludovic Noels
University of Liège and University of Liège
Downloads 77 (837,727)

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Deep-Material Network, Convolutional Neural Network, Data-Driven, Stochastic Multi-Scale, Composites

4.

High-Dimensional Parameter Identification with Bayesian Inference for Finite-Strain Visco-Elastic-Visco-Plastic Modeling of Selective Laser Sintering (Sls) Polyamide 12 (Pa12) and Neural Net-Work (Nnw)-Based Material Parameter Generator

Number of pages: 46 Posted: 23 May 2023
Ling Wu, Cyrielle Anglade, Lucia Cobian, Miguel Monclús, Javier Segurado, Fatma Karayagiz, Ubiratan Freitas and Ludovic Noels
University of Liège, University of Liège, IMDEA Materials Institute, IMDEA Materials Institute, Universidad Politécnica de Madrid, affiliation not provided to SSRN, affiliation not provided to SSRN and University of Liège
Downloads 60 (961,006)

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Bayesian inference, Visco-elasticity-visco-plasticity, selective laser sintering, Polyamide

5.

Discrete non-local interactions formulation of deep material networks for damage-enhanced constitutive material models

Number of pages: 50 Posted: 08 Apr 2026
Ling Wu and Ludovic Noels
University of Liège and University of Liège
Downloads 35 (1,385,945)

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Deep Material Network, Diffuse damage, Composite, Data-driven

6.

An encapsulating non-local framework for data-driven multiscale simulations of composite material failure

Number of pages: 46 Posted: 07 Apr 2026
Cyrielle Anglade, Ling Wu, Guerric Lemoine and Ludovic Noels
affiliation not provided to SSRN, University of Liège, affiliation not provided to SSRN and University of Liège
Downloads 19 (1,470,786)

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Multiscale, Recurrent Neural Network, Non-local damage, Data-driven, composites