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INIS
neural networks
100%
simulation
87%
graphs
75%
prediction
75%
layers
75%
alloys
75%
monte carlo method
75%
heat transfer
75%
distribution
75%
nanoparticles
75%
data
68%
thermal conductivity
56%
radiative transfer
50%
scattering
50%
suppression
37%
transport
37%
output
24%
absorption
24%
probabilistic estimation
24%
particle size
24%
anisotropy
24%
yields
24%
flow models
24%
optical properties
24%
accuracy
24%
competition
18%
density functional method
18%
dispersions
18%
molecular dynamics method
18%
optical modes
18%
range
18%
fluctuations
18%
modeling
18%
thermal degradation
18%
physics
18%
crystals
18%
phonons
18%
breakdown
18%
solids
18%
heat capacity
18%
acoustics
18%
potentials
18%
occupations
18%
heat
18%
Keyphrases
Monte Carlo Prediction
75%
Embedding Layer
75%
Conditional Normalizing Flow
75%
InP Nanoparticles
75%
Radiative Properties
75%
Neural Network Interatomic Potentials
75%
Equivariant Graph Neural Network
75%
Radiative Transfer Modeling
50%
Posterior Predictive Distribution
24%
Normalizing Flow Models
24%
Probabilistic Data
24%
Conventional Neural Network
24%
Optical Properties
24%
Uncertainty Estimation
24%
Training Data
24%
Conditional Distribution
24%
Particle Size Distribution
24%
Anisotropy Factor
24%
Monte Carlo Radiative Transfer
24%
Scattering Media
24%
Scattering Coefficient
24%
Absorption Coefficient
24%
Transmittance
24%
Prediction Accuracy
24%
Nanoparticle Properties
24%
Absorbance
24%
Data-driven Surrogate Model
24%
Uncertainty Quantification
24%
Model Use
24%
Bond Disorder
24%
Mie Theory
24%
Mass Contrast
24%
Optical Output
24%
Physics
Alloy
75%
Neural Network
75%
Transport Property
75%
Thermal Conductivity
75%
Diffusion
50%
Transportation
50%
Density Functional Theory
24%
Physics
24%
Alloying
24%
Crystals
24%
Specific Heat
24%
Molecular Dynamics
24%
Elevated Temperature
24%
Phonon
24%
Thermal Degradation
24%
Acoustics
24%