Using NURBs-Based Metamodels as Surrogate Spine Models for More Efficient Probabilistic Analysis
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Summary
This research investigates the use of Non-Uniform Rational B-splines based metamodels to reduce the cost of expensive probabilistic simulation models of the spine for analysis and optimization and finds a promising approach for reducing the computational time of running a Monte Carlo simulation.
- Type
- article
- Published
- 2011-01-01
- Cited by
- 0
- References
- 19
- OpenAlex
- https://openalex.org/W2325166151
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:113366060
Keywords
Probabilistic logic, Computer science, Metamodeling, Monte Carlo method, Statistical model
References
- HyPerModels: Hyperdimensional Performance Models for engineering design
- Reliability and performance-based design: a computational approach and applications
- Load-Sharing Between Anterior and Posterior Elements in a Lumbar Motion Segment Implanted With an Artificial Disc
- A Sampling-Based Approach for Probabilistic Design with Random Fields
- A fast and efficient response surface approach for structural reliability problems
- Reliability-based design optimization using a moment method and a kriging metamodel
- Selecting an Appropriate Metamodel: The Case for NURBs Metamodels
- Application Of Kriging Method To Structural Reliability Problems
- N -Dimensional Nonuniform Rational B-Splines for Metamodeling
- A survey on approaches for reliability-based optimization
- Neural network-based simulation metamodels for predicting probability distributions
- The use of metamodeling techniques for optimization under uncertainty
- A Global Robust Optimization Using Kriging Based Approximation Model
- Robust Optimization Exploration Using NURBs-Based Metamodeling Techniques
- Analytical Uncertainty Propagation via Metamodels in Simulation-Based Design under Uncertainty
- On Using Kriging Models as Probabilistic Models in Design
- A Metamodeling Method Based on Support Vector Regression for Robust Optimization
- Simulation metamodels for modeling output distribution parameters
- Computing confidence intervals for stochastic simulation using neural network metamodels
- A Metamodeling Method Based on Support Vector Regression for Robust Optimization
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