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Accelerating finite element analysis using machine learning
F. Ghavamian
Applied Mechanics
Research output
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Thesis
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Dissertation (TU Delft)
88
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Dive into the research topics of 'Accelerating finite element analysis using machine learning'. Together they form a unique fingerprint.
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INIS
finite element method
100%
machine learning
100%
interpolation
50%
simulation
41%
decomposition
33%
equations
25%
data
25%
applications
16%
geometry
16%
neural networks
16%
speed
16%
intrusion
16%
images
16%
solutions
16%
design
8%
acceleration
8%
efficiency
8%
electrochemistry
8%
processing
8%
evaluation
8%
lithium ion batteries
8%
interactions
8%
output
8%
nonlinear problems
8%
vectors
8%
physics
8%
instability
8%
algorithms
8%
strains
8%
sampling
8%
Engineering
Models
100%
Finite Element Method
100%
Fields
33%
Constitutive Equation
16%
Source Coding
16%
Images
16%
Point Method
8%
Clustering Algorithm
8%
Time Domain
8%
Uncertainty Quantification
8%
Strain Data
8%
Data Point
8%
Industrial Applications
8%
Applicability
8%
Processing
8%
Mechanical Interaction
8%
Force Vector
8%
Design
8%
Recurrent Neural Network
8%
Simulation Time
8%
Chemical Engineering
Learning System
100%
Neural Network
33%
Recurrent Neural Network
33%
Linear Systems
33%
Material Science
Finite Element Method
100%
Proper Orthogonal Decomposition
33%
Battery (Electrochemical Energy Engineering)
16%
Lithium Ion Battery
8%
Mathematics
Discrete Empirical Interpolation Method
50%
Geometry
16%
Open Question
8%
Computational
8%
Decomposition Method
8%
Points
8%
Real-Time Simulation
8%
Systems of Linear Equation
8%
Nonlinear
8%
Physics
Images
16%
Utilization
16%
Speed
16%
Battery
16%
Physics
8%
Li-Ion Battery
8%
Shapes
8%