A state-of-the-art review on machine learning-based multiscale modeling, simulation, homogenization and design of materials
Multiscale simulation and homogenization of materials have become the major
computational technology as well as engineering tools in material modeling and material …
computational technology as well as engineering tools in material modeling and material …
Machine-learning and high-throughput studies for high-entropy materials
The combination of multiple-principal element materials, known as high-entropy materials
(HEMs), expands the multi-dimensional compositional space to gigantic stoichiometry. It is …
(HEMs), expands the multi-dimensional compositional space to gigantic stoichiometry. It is …
Artificial-intelligence-led revolution of construction materials: From molecules to Industry 4.0
Industry 4.0 promotes the transformation of manufacturing industry to intelligence, which
demands advances in materials, devices, and systems of the construction industry …
demands advances in materials, devices, and systems of the construction industry …
Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering
In this work, a unified AI-framework named Hierarchical Deep Learning Neural Network
(HiDeNN) is proposed to solve challenging computational science and engineering …
(HiDeNN) is proposed to solve challenging computational science and engineering …
A learning-based multiscale method and its application to inelastic impact problems
The macroscopic properties of materials that we observe and exploit in engineering
application result from complex interactions between physics at multiple length and time …
application result from complex interactions between physics at multiple length and time …
[HTML][HTML] A perspective on the microscopic pressure (stress) tensor: History, current understanding, and future challenges
The pressure tensor (equivalent to the negative stress tensor) at both microscopic and
macroscopic levels is fundamental to many aspects of engineering and science, including …
macroscopic levels is fundamental to many aspects of engineering and science, including …
Multiscale modeling of materials: Computing, data science, uncertainty and goal-oriented optimization
The recent decades have seen various attempts at accelerating the process of developing
materials targeted towards specific applications. The performance required for a particular …
materials targeted towards specific applications. The performance required for a particular …
Machine learning-assisted wood materials: Applications and future prospects
Wood and wood-based materials, surpassing their conventional image as mere stems and
branches of trees, have found extensive utilization in diverse industrial sectors due to their …
branches of trees, have found extensive utilization in diverse industrial sectors due to their …
Machine Learning in Computer Aided Engineering
The extraordinary success of Machine Learning (ML) in many complex heuristic fields has
promoted its introduction in more analytical engineering fields, improving or substituting …
promoted its introduction in more analytical engineering fields, improving or substituting …
Manifold learning for coarse-graining atomistic simulations: Application to amorphous solids
We introduce a generalized machine learning framework to probabilistically parameterize
upper-scale models in the form of nonlinear PDEs consistent with a continuum theory, based …
upper-scale models in the form of nonlinear PDEs consistent with a continuum theory, based …