Artificial intelligence and machine learning in design of mechanical materials
Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms,
is becoming an important tool in the fields of materials and mechanical engineering …
is becoming an important tool in the fields of materials and mechanical engineering …
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 …
Inverse design of shell-based mechanical metamaterial with customized loading curves based on machine learning and genetic algorithm
Y Wang, Q Zeng, J Wang, Y Li, D Fang - Computer Methods in Applied …, 2022 - Elsevier
Triply periodic minimal surfaces (TPMSs) have attracted great attention due to their distinct
advantages such as high strength and light weight compared to traditional lattice structures …
advantages such as high strength and light weight compared to traditional lattice structures …
Modeling finite-strain plasticity using physics-informed neural network and assessment of the network performance
Physics-informed neural networks (PINN) can solve partial differential equations (PDEs) by
encoding the mathematical information explicitly into the loss functions. In the context of …
encoding the mathematical information explicitly into the loss functions. In the context of …
A physics-guided neural network framework for elastic plates: Comparison of governing equations-based and energy-based approaches
One of the obstacles hindering the scaling-up of the initial successes of machine learning in
practical engineering applications is the dependence of the accuracy on the size and quality …
practical engineering applications is the dependence of the accuracy on the size and quality …
On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling
Data-driven constitutive modeling is an emerging field in computational solid mechanics
with the prospect of significantly relieving the computational costs of hierarchical …
with the prospect of significantly relieving the computational costs of hierarchical …
[HTML][HTML] Deep active learning for constitutive modelling of granular materials: From representative volume elements to implicit finite element modelling
Constitutive relation remains one of the most important, yet fundamental challenges in the
study of granular materials. Instead of using closed-form phenomenological models or …
study of granular materials. Instead of using closed-form phenomenological models or …
Discovering plasticity models without stress data
We propose an approach for data-driven automated discovery of material laws, which we
call EUCLID (Efficient Unsupervised Constitutive Law Identification and Discovery), and we …
call EUCLID (Efficient Unsupervised Constitutive Law Identification and Discovery), and we …
The mixed deep energy method for resolving concentration features in finite strain hyperelasticity
The introduction of Physics-informed Neural Networks (PINNs) has led to an increased
interest in deep neural networks as universal approximators of PDEs in the solid mechanics …
interest in deep neural networks as universal approximators of PDEs in the solid mechanics …
Modular machine learning-based elastoplasticity: Generalization in the context of limited data
The development of highly accurate constitutive models for materials that undergo path-
dependent processes continues to be a complex challenge in computational solid …
dependent processes continues to be a complex challenge in computational solid …