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1
Hierarchical materials informatics : novel analytics for materials data
Butterworth-Heinemann is an imprint of Elsevier
Surya R. Kalidindi Ph.D
microstructure
materials
function
figure
linkages
microstructures
correlations
interest
mater
values
crystal
volume
selected
statistics
approach
strain
specific
shown
scale
kalidindi
functions
elastic
statistical
representations
defined
framework
tensor
mks
dfts
models
dft
element
composite
measures
reference
computational
presented
pca
stress
corresponding
dimensional
noted
hierarchical
matrix
methods
scales
expressed
internal
quantification
structures
Year:
2015
Language:
english
File:
PDF, 17.59 MB
Your tags:
0
/
0
english, 2015
2
Informatics for Materials Science and Engineering. Data-driven Discovery for Accelerated Experimentation and Application
Krishna Rajan (Eds.)
materials
figure
analysis
methods
descriptors
models
modeling
approach
combinatorial
informatics
chemical
function
visualization
algorithms
experimental
pca
engineering
shown
systems
sensing
values
techniques
response
sci
experiments
microstructure
mining
temperature
dimensional
molecular
potyrailo
surface
mater
parameters
algorithm
component
dimensionality
cluster
optimization
linear
reduction
databases
method
discovery
processing
polymer
variables
clustering
components
composition
Year:
2013
Language:
english
File:
PDF, 38.35 MB
Your tags:
0
/
0
english, 2013
3
Crystal Plasticity Finite Element Methods: in Materials Science and Engineering
Wiley-VCH
Franz Roters
,
Philip Eisenlohr
,
Thomas R. Bieler
,
Dierk Raabe
plasticity
crystal
element
grain
finite
raabe
deformation
roters
slip
boundary
methods
strain
dislocation
bieler
eisenlohr
figure
stress
dierk
franz
philip
tex
chap
texture
shear
mater
cpfe
models
constitutive
boundaries
plastic
dislocations
orientation
method
mechanical
simulation
elastic
gradient
density
nucleation
simulations
twinning
acta
grains
modeling
sci
twin
materials
plane
evolution
systems
Year:
2010
Language:
english
File:
PDF, 4.14 MB
Your tags:
5.0
/
0
english, 2010
4
Graph neural networks for efficient learning of mechanical properties of polycrystals
Elsevier B.V.
Jonathan M. Hestroffer
gnn
graph
microstructure
materials
dx.doi.org
stiffness
grain
url
textures
𝑘
microstructures
node
polycrystalline
validation
graphs
computational
mater
networks
neural
hyper
models
texture
𝑣
accuracy
layers
values
cnns
effective
grains
layer
mechanical
mves
kalidindi
maxare
pii
regression
s.r
www.sciencedirect.com
architecture
meanare
modeling
orientation
convolutional
features
hestroffer
inference
parameters
passing
prediction
representations
Year:
2022
Language:
english
File:
PDF, 2.12 MB
Your tags:
0
/
0
english, 2022
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