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Model order reduction techniques to identify submarining risk in a simplified human body model
L. Go
, J. S. Jehle
, M. Rees
, C. Czech
, S. Peldschus
,
F. Duddeck
Associate Professorship of Computational Mechanics
Technical University of Munich
Innovations
University of Munich
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peer-review
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Keyphrases
Dimensionality Reduction
100%
Model Order Reduction
100%
Non-intrusive
100%
Submarine
100%
Human Body Model
100%
Computationally Expensive
50%
Simulation Method
50%
Order of Magnitude
50%
Machine Learning Based
50%
Surrogate Model
50%
Uncertainty Quantification
50%
Sensitivity Analysis
50%
Nonlinear Dimensionality Reduction
50%
Design Space
50%
Black-box Model
50%
Optimization Study
50%
High-fidelity Simulation
50%
Sensitivity Optimization
50%
Crash Simulation
50%
Parametric Reduced Order Model
50%
Engineering
Dimensionality Reduction Technique
100%
Human Body Model
100%
Reduced Order Model
50%
Uncertainty Quantification
50%
Surrogate Model
50%
Simulation Method
50%
Black-Box Model
50%
Design Space
50%
Learning System
50%
Chemical Engineering
Learning System
100%