[PDF] Probabilistic biomechanical finite element simulations: whole-model classical hypothesis testing based on upcrossing geometry | Semantic Scholar Skip to search formSkip to main content> Semantic Scholar's Logo Search Sign InCreate Free Account You are currently offline. Some features of the site may not work correctly. DOI:10.7717/peerj-cs.96 Corpus ID: 32214297Probabilistic biomechanical finite element simulations: whole-model classical hypothesis testing based on upcrossing geometry @article{Pataky2016ProbabilisticBF, title={Probabilistic biomechanical finite element simulations: whole-model classical hypothesis testing based on upcrossing geometry}, author={T. Pataky and M. Koseki and P. G. Cox}, journal={PeerJ Comput. Sci.}, year={2016}, volume={2}, pages={e96} } T. Pataky, M. Koseki, P. G. Cox Published 2016 Computer Science PeerJ Comput. Sci. Statistical analyses of biomechanical finite element (FE) simulations are frequently conducted on scalar metrics extracted from anatomically homologous regions, like maximum von Mises stresses from demarcated bone areas. The advantages of this approach are numerical tabulability and statistical simplicity, but disadvantages include region demarcation subjectivity, spatial resolution reduction, and results interpretation complexity when attempting to mentally map tabulated results to original… Expand View via Publisher peerj.com Save to Library Create Alert Cite Launch Research Feed Share This Paper 2 Citations View All Figures, Tables, and Topics from this paper figure 1 figure 2 table 2 figure 3 figure 4 figure 5 figure 6 figure 7 figure 8 figure 9 figure 10 figure 11 figure 12 figure 13 figure 14 View All 15 Figures & Tables Simulation Finite element method Experiment Homology (biology) Table (database) Quantum field theory Computation Demarcation point Numerical analysis Triune continuum paradigm Iterative method Request for tender Computational anatomy Stress ball 2 Citations Citation Type Citation Type All Types Cites Results Cites Methods Cites Background Has PDF Publication Type Author More Filters More Filters Filters Sort by Relevance Sort by Most Influenced Papers Sort by Citation Count Sort by Recency A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling P. 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