On the use of artificial neural networks in topology optimisation
RV Woldseth, N Aage, JA Bærentzen… - Structural and …, 2022 - Springer
The question of how methods from the field of artificial intelligence can help improve the
conventional frameworks for topology optimisation has received increasing attention over …
conventional frameworks for topology optimisation has received increasing attention over …
Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation
The ever-increasing demand for novel materials with superior properties inspires retrofitting
traditional research paradigms in the era of artificial intelligence and automation. An …
traditional research paradigms in the era of artificial intelligence and automation. An …
Computational design and manufacturing of sustainable materials through first-principles and materiomics
Engineered materials are ubiquitous throughout society and are critical to the development
of modern technology, yet many current material systems are inexorably tied to widespread …
of modern technology, yet many current material systems are inexorably tied to widespread …
[HTML][HTML] Inverse design of truss lattice materials with superior buckling resistance
Manipulating the architecture of materials to achieve optimal combinations of properties
(inverse design) has always been the dream of materials scientists and engineers. Lattices …
(inverse design) has always been the dream of materials scientists and engineers. Lattices …
[HTML][HTML] Scope of machine learning in materials research—A review
This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …
materials research across six key dimensions, redefining the field's boundaries. It explains …
Deep learning aided inverse design of the buckling-guided assembly for 3D frame structures
Buckling-guided assembly of three-dimensional (3D) mesostructures from pre-defined 2D
precursor patterns has arisen increasing attention, owing to the compelling advantages in …
precursor patterns has arisen increasing attention, owing to the compelling advantages in …
[HTML][HTML] Machine learning assisted design of shape-programmable 3D kirigami metamaterials
Kirigami-engineering has become an avenue for realizing multifunctional metamaterials that
tap into the instability landscape of planar surfaces embedded with cuts. Recently, it has …
tap into the instability landscape of planar surfaces embedded with cuts. Recently, it has …
[HTML][HTML] Prediction and validation of the transverse mechanical behavior of unidirectional composites considering interfacial debonding through convolutional neural …
In this work, we propose a prediction model of the transverse mechanical behavior of
unidirectional (UD) composites containing complex microstructure with the help of a …
unidirectional (UD) composites containing complex microstructure with the help of a …
Physics-guided, physics-informed, and physics-encoded neural networks and operators in scientific computing: Fluid and solid mechanics
SA Faroughi, NM Pawar… - Journal of …, 2024 - asmedigitalcollection.asme.org
Advancements in computing power have recently made it possible to utilize machine
learning and deep learning to push scientific computing forward in a range of disciplines …
learning and deep learning to push scientific computing forward in a range of disciplines …
Into the unknown: how computation can help explore uncharted material space
Novel functional materials are urgently needed to help combat the major global challenges
facing humanity, such as climate change and resource scarcity. Yet, the traditional …
facing humanity, such as climate change and resource scarcity. Yet, the traditional …