Machine learning meta-models for fast parameter identification of the lattice discrete particle model
DOI10.1007/s00466-023-02320-zzbMath1526.74072OpenAlexW4362633499MaRDI QIDQ6164296
Gianluca Cusatis, Madura Pathirage, Yuhui Lyu, Elham Ramyar, Wing Kam Liu
Publication date: 27 July 2023
Published in: Computational Mechanics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00466-023-02320-z
optimizationunconfined compressiontensile fracturecoarse aggregate concrete modelhybrid inverse analysishydrostatic fracturemachine learning based model
Learning and adaptive systems in artificial intelligence (68T05) Composite and mixture properties (74E30) Fracture and damage (74R99) Inverse problems in equilibrium solid mechanics (74G75) Numerical and other methods in solid mechanics (74S99)
Cites Work
- All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
- An effective computational tool for parametric studies and identification problems in materials mechanics
- Characterisation of materials subjected to large strains by inverse modelling based on in-plane displacement fields
- Confinement-shear lattice CSL model for fracture propagation in concrete
- Estimation of in vivo constitutive parameters of the aortic wall using a machine learning approach
- Image-based modelling for adolescent idiopathic scoliosis: mechanistic machine learning analysis and prediction
- A neural network-aided Bayesian identification framework for multiscale modeling of nanocomposites
- A coupled damage–plasticity model for concrete based on thermodynamic principles: Part I: model formulation and parameter identification
- New perspective of fracture mechanics inspired by gap test with crack-parallel compression
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