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Multi-fidelity Data Aggregation using Convolutional Neural Networks
Jie Chen, Yi Gao,
Yongming Liu
Engineering, Ira A. Fulton Schools of (IAFSE)
Research output
:
Contribution to journal
›
Article
›
peer-review
12
Scopus citations
Overview
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Dive into the research topics of 'Multi-fidelity Data Aggregation using Convolutional Neural Networks'. Together they form a unique fingerprint.
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Mathematics
Data Aggregation
100%
Fidelity
73%
Neural Networks
54%
Framework
9%
High Accuracy
5%
Relationships
4%
Hybrid Simulation
4%
Multiscale Simulation
4%
Convolution
4%
Engineering
4%
Testing
3%
Nonlinear Mapping
3%
High Resolution
3%
Multiresolution
3%
High-dimensional Data
3%
Simulation Experiment
3%
Imaging
3%
Architecture
3%
Error Estimation
3%
Local Field
2%
Computational Cost
2%
Experimental Data
2%
Aggregation
2%
Collocation Method
2%
Flexibility
2%
Interpolate
2%
Costs
2%
Strategy
1%
Observation
1%
Modeling
1%
Simulation
1%
Numerical Examples
1%
Derivative
1%
Range of data
1%
Form
1%
Model
0%
Engineering & Materials Science
Convolutional neural networks
52%
Agglomeration
50%
Convolution
16%
Testing
8%
Deep neural networks
8%
Error analysis
7%
Interpolation
7%
Derivatives
6%
Imaging techniques
6%
Costs
6%
Neural networks
5%
Experiments
2%
Physics & Astronomy
convolution integrals
5%
simulation
3%
costs
3%
engineering
3%
collocation
2%
interpolation
2%
flexibility
2%
high resolution
1%