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 …
Recurrent neural networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step
Abstract Artificial Neural Networks (NNWs) are appealing functions to substitute high
dimensional and non-linear history-dependent problems in computational mechanics since …
dimensional and non-linear history-dependent problems in computational mechanics since …
Multiscale computational solid mechanics: data and machine learning
Multiscale computational solid mechanics concurrently connects complex material physics
and macroscopic structural analysis to accelerate the application of advanced materials in …
and macroscopic structural analysis to accelerate the application of advanced materials in …
Rapid inverse calibration of a multiscale model for the viscoplastic and creep behavior of short fiber-reinforced thermoplastics based on Deep Material Networks
In this work, we propose to use deep material networks (DMNs) as a surrogate model for full-
field computational homogenization to inversely identify material parameters of constitutive …
field computational homogenization to inversely identify material parameters of constitutive …
LS-DYNA machine learning–based multiscale method for nonlinear modeling of short fiber–reinforced composites
Short fiber–reinforced composites (SFRCs) are high-performance engineering materials for
lightweight structural applications in the automotive and electronics industries. Typically …
lightweight structural applications in the automotive and electronics industries. Typically …
Deep material network via a quilting strategy: visualization for explainability and recursive training for improved accuracy
Recent developments integrating micromechanics and neural networks offer promising
paths for rapid predictions of the response of heterogeneous materials with similar accuracy …
paths for rapid predictions of the response of heterogeneous materials with similar accuracy …
A hybrid algorithm of particle swarm optimization and finite element method to identify local mesoscopic damage of concrete-like materials
B Sun, Y Li, T Guo - Mechanics of Materials, 2023 - Elsevier
Meso-scale material modeling and parameter identification are still unsettled issues in the
current multi-scale methods for the field of computational mechanics. In this investigation, a …
current multi-scale methods for the field of computational mechanics. In this investigation, a …
Micromechanics-informed parametric deep material network for physics behavior prediction of heterogeneous materials with a varying morphology
T Li - Computer Methods in Applied Mechanics and …, 2024 - Elsevier
Abstract Deep Material Network (DMN) has recently emerged as a data-driven surrogate
model for heterogeneous materials. Given a particular microstructural morphology, the …
model for heterogeneous materials. Given a particular microstructural morphology, the …
Micro-mechanics and data-driven based reduced order models for multi-scale analyses of woven composites
Order reduction of woven composite materials is based on the definition of short fibre
reinforced matrix material pseudo-grains completed by pure matrix parts. The former ones …
reinforced matrix material pseudo-grains completed by pure matrix parts. The former ones …
Training deep material networks to reproduce creep loading of short fiber-reinforced thermoplastics with an inelastically-informed strategy
Deep material networks (DMNs) are a recent multiscale technology which enable running
concurrent multiscale simulations on industrial scale with the help of powerful surrogate …
concurrent multiscale simulations on industrial scale with the help of powerful surrogate …