Probabilistic simulation methods have allowed for many advancements in the field of biomechanics, especially for the human spine. To accurately model a complex system such as the spine, the model must account for the differences that occur from one specimen to the next. These differences in material properties and anatomical shapes are described probabilistically. Accurately modeling the effects of these differences is important in biomechanics as no two people are exactly alike, yet building individual models of every person is impractical. Several authors have conducted research into more accurate ways to model biomechanical systems such as the spine, however the computational expense of performing analysis and optimization with these probabilistic simulation models still remains an issue, particularly with respect to the underlying Monte Carlo simulations. The research described in this paper investigates the use of Non-Uniform Rational B-splines (NURBs) based metamodels to reduce the cost of expensive probabilistic simulation models of the spine for analysis and optimization. Metamodels are simply mathematical approximations of a model or in other words, a model of models. Metamodels are widely used to represent the behavior of complex systems based on limited data from the original system model. Metamodels are often more computationally efficient to store and analyze than the original system models which they approximate. Using a Functional Spinal Unit (FSU) Finite Element Model, two different probabilistic NURBs-based metamodeling methods were developed and tested. Through the use of metamodels, a promising approach for reducing the computational time of running a Monte Carlo simulation was discovered.

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