Application of an adaptive polynomial chaos expansion on computationally expensive three-dimensional cardiovascular models for uncertainty quantification and sensitivity analysis
| Autor: | WP Wouter Donders, Wouter Huberts, Emiel M.J. van Disseldorp, Richard G.P. Lopata, Tammo Delhaas, Barend Mees, Frans N. van de Vosse, Kujtim Gashi, Sjeng Quicken |
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| Přispěvatelé: | Cardiovascular Biomechanics, RS: CARIM - R2.09 - Cardiovascular system dynamics, Biomedische Technologie, Promovendi CD, RS: MHeNs - R3 - Neuroscience, Promovendi MHN, MUMC+: MA Med Staf Spec Vaatchirurgie (9), RS: CARIM - R3.08 - Regenerative and reconstructive medicine for vascular disease, Surgery, MUMC+: MA Vaatchirurgie CVC (3) |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Patient-Specific Modeling
Computer science 0206 medical engineering Biomedical Engineering Blood Pressure Field (mathematics) 02 engineering and technology 030204 cardiovascular system & hematology Sensitivity and Specificity 03 medical and health sciences 0302 clinical medicine Elastic Modulus Physiology (medical) Humans Applied mathematics Computer Simulation Aorta Abdominal Finite series Sensitivity (control systems) Uncertainty quantification Polynomial chaos Adaptive algorithm Models Cardiovascular Reproducibility of Results Numerical Analysis Computer-Assisted 020601 biomedical engineering Finite element method Nonlinear Dynamics Flow (mathematics) Shear Strength Algorithms Blood Flow Velocity Aortic Aneurysm Abdominal |
| Zdroj: | Journal of Biomechanical Engineering : Transactions of the ASME, 138(12):121010. American Society of Mechanical Engineers Journal of Biomechanical Engineering-Transactions of the Asme, 138(12). American Society of Mechanical Engineers(ASME) |
| ISSN: | 0148-0731 |
| Popis: | When applying models to patient-specific situations, the impact of model input uncertainty on the model output uncertainty has to be assessed. Proper uncertainty quantification (UQ) and sensitivity analysis (SA) techniques are indispensable for this purpose. An efficient approach for UQ and SA is the generalized polynomial chaos expansion (gPCE) method, where model response is expanded into a finite series of polynomials that depend on the model input (i.e., a meta-model). However, because of the intrinsic high computational cost of three-dimensional (3D) cardiovascular models, performing the number of model evaluations required for the gPCE is often computationally prohibitively expensive. Recently, Blatman and Sudret (2010, “An Adaptive Algorithm to Build Up Sparse Polynomial Chaos Expansions for Stochastic Finite Element Analysis,” Probab. Eng. Mech., 25(2), pp. 183–197) introduced the adaptive sparse gPCE (agPCE) in the field of structural engineering. This approach reduces the computational cost with respect to the gPCE, by only including polynomials that significantly increase the meta-model’s quality. In this study, we demonstrate the agPCE by applying it to a 3D abdominal aortic aneurysm (AAA) wall mechanics model and a 3D model of flow through an arteriovenous fistula (AVF). The agPCE method was indeed able to perform UQ and SA at a significantly lower computational cost than the gPCE, while still retaining accurate results. Cost reductions ranged between 70–80% and 50–90% for the AAA and AVF model, respectively. |
| Databáze: | OpenAIRE |
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