Empowering Materials Processing and Performance from Data and AI

Empowering Materials Processing and Performance from Data and AI

Third millennium engineering address new challenges in materials sciences and engineering. In particular, the advances in materials engineering combined with the advances in data acquisition, processing and mining as well as artificial intelligence allow for new ways of thinking in designing new mat...

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Bibliografische gegevens
Andere auteurs: Chinesta, Francisco (Redacteur), Cueto, Elías (Redacteur), Klusemann, Benjamin (Redacteur)
Formaat: Hoofdstuk
Gepubliceerd in: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
Onderwerpen:
Technology: general issues
plasticity
machine learning
constitutive modeling
manifold learning
topological data analysis
GENERIC
soft living tissues
hyperelasticity
computational modeling
data-driven mechanics
TDA
Code2Vect
nonlinear regression
effective properties
microstructures
model calibration
sensitivity analysis
elasto-visco-plasticity
Gaussian process
high-throughput experimentation
additive manufacturing
Ti-Mn alloys
spherical indentation
statistical analysis
Gaussian process regression
nanoporous metals
open-pore foams
FE-beam model
data mining
mechanical properties
hardness
principal component analysis
structure-property relationship
microcompression
nanoindentation
analytical model
finite element model
artificial neural networks
model correction
feature engineering
physics based
data driven
laser shock peening
residual stresses
data-driven
multiscale
nonlinear
stochastics
neural networks
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