A review on data-driven constitutive laws for solids
This review article highlights state-of-the-art data-driven techniques to discover, encode,
surrogate, or emulate constitutive laws that describe the path-independent and path …
surrogate, or emulate constitutive laws that describe the path-independent and path …
State-of-the-art review of machine learning applications in constitutive modeling of soils
P Zhang, ZY Yin, YF Jin - Archives of Computational Methods in …, 2021 - Springer
Abstract Machine learning (ML) may provide a new methodology to directly learn from raw
data to develop constitutive models for soils by using pure mathematic skills. It has …
data to develop constitutive models for soils by using pure mathematic skills. It has …
Clustering discretization methods for generation of material performance databases in machine learning and design optimization
Mechanical science and engineering can use machine learning. However, data sets have
remained relatively scarce; fortunately, known governing equations can supplement these …
remained relatively scarce; fortunately, known governing equations can supplement these …
[HTML][HTML] A new stable inverse method for identification of the elastic constants of a three-dimensional generally anisotropic solid
This article presents a new approach for inverse identification of all elastic constants of a 3D
generally anisotropic solid with arbitrary geometry via measured strain data. To eradicate …
generally anisotropic solid with arbitrary geometry via measured strain data. To eradicate …
Integration of laboratory testing and constitutive modeling of soils
Q Fu, YMA Hashash, S Jung, J Ghaboussi - Computers and Geotechnics, 2007 - Elsevier
A soil constitutive model that correctly captures soil behavior under general loading modes
is requisite to solving complex boundary value geotechnical engineering problems …
is requisite to solving complex boundary value geotechnical engineering problems …
Non-sampling inverse stochastic numerical–experimental identification of random elastic material parameters in composite plates
K Sepahvand, S Marburg - Mechanical Systems and Signal Processing, 2015 - Elsevier
A non-sampling probability identification method based on the generalized polynomial
chaos (gPC) expansion is adopted for estimating random parameters of composite plates …
chaos (gPC) expansion is adopted for estimating random parameters of composite plates …
Developing constitutive models from EPR‐based self‐learning finite element analysis
A constitutive model that captures the material behavior under a wide range of loading
conditions is essential for simulating complex boundary value problems. In recent years …
conditions is essential for simulating complex boundary value problems. In recent years …
Neural network constitutive modelling for non‐linear characterization of anisotropic materials
H Man, T Furukawa - International journal for numerical …, 2011 - Wiley Online Library
This paper presents a new technique of neural network constitutive modelling for non‐linear
characterization of anisotropic materials. The proposed technique, based on a recently …
characterization of anisotropic materials. The proposed technique, based on a recently …
Data-driven design optimization for composite material characterization
JG Michopoulos, JC Hermanson, A Iliopoulos… - 2011 - asmedigitalcollection.asme.org
The main goal of the present paper is to demonstrate the value of design optimization
beyond its use for structural shape determination in the realm of the constitutive …
beyond its use for structural shape determination in the realm of the constitutive …
Analysis of behaviour of soils under cyclic loading using EPR-based finite element method
In this paper, a new approach is presented for modelling of behaviour of soils in finite
element analysis under cyclic loading. This involves development of a unified approach to …
element analysis under cyclic loading. This involves development of a unified approach to …