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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 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 …
Deep learning model to predict complex stress and strain fields in hierarchical composites
Materials-by-design is a paradigm to develop previously unknown high-performance
materials. However, finding materials with superior properties is often computationally or …
materials. However, finding materials with superior properties is often computationally or …
A bio-based nanofibre hydrogel filter for sustainable water purification
M Jiang, C **g, C Lei, X Han, Y Wu, S Ling… - Nature …, 2024 - nature.com
Removal of suspended solids (SS) is a prerequisite for delivering clean water. However,
removal of ultrafine SS during water purification in a cost-effective manner remains a global …
removal of ultrafine SS during water purification in a cost-effective manner remains a global …
Machine learning‐driven biomaterials evolution
Biomaterials is an exciting and dynamic field, which uses a collection of diverse materials to
achieve desired biological responses. While there is constant evolution and innovation in …
achieve desired biological responses. While there is constant evolution and innovation in …
Deep learning in computational mechanics: a review
The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …
Taking the leap between analytical chemistry and artificial intelligence: A tutorial review
The last 10 years have witnessed the growth of artificial intelligence into different research
areas, emerging as a vibrant discipline with the capacity to process large amounts of …
areas, emerging as a vibrant discipline with the capacity to process large amounts of …
End-to-end deep learning method to predict complete strain and stress tensors for complex hierarchical composite microstructures
Due to the high demand for materials with superior mechanical properties and diverse
functions, designing composite materials is an integral part in materials development …
functions, designing composite materials is an integral part in materials development …
Pragmatic generative optimization of novel structural lattice metamaterials with machine learning
Metamaterials, otherwise known as architected or programmable materials, enable
designers to tailor mesoscale topology and shape to achieve unique material properties that …
designers to tailor mesoscale topology and shape to achieve unique material properties that …
A stochastic multiscale method for the prediction of the thermal conductivity of Polymer nanocomposites through hybrid machine learning algorithms
In this paper, we propose a hybrid machine learning method to predict the thermal
conductivity of polymeric nanocomposites (PNCs). Therefore, a combination of artificial …
conductivity of polymeric nanocomposites (PNCs). Therefore, a combination of artificial …