Abstract
AbstractImage-based simulation, the use of 3D images to calculate physical quantities, relies on image segmentation for geometry creation. However, this process introduces image segmentation uncertainty because different segmentation tools (both manual and machine-learning-based) will each produce a unique and valid segmentation. First, we demonstrate that these variations propagate into the physics simulations, compromising the resulting physics quantities. Second, we propose a general framework for rapidly quantifying segmentation uncertainty. Through the creation and sampling of segmentation uncertainty probability maps, we systematically and objectively create uncertainty distributions of the physics quantities. We show that physics quantity uncertainty distributions can follow a Normal distribution, but, in more complicated physics simulations, the resulting uncertainty distribution can be surprisingly nontrivial. We establish that bounding segmentation uncertainty can fail in these nontrivial situations. While our work does not eliminate segmentation uncertainty, it improves simulation credibility by making visible the previously unrecognized segmentation uncertainty plaguing image-based simulation.
Funder
DOE | LDRD | Sandia National Laboratories
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry
Reference66 articles.
1. National Research Council. Integrated Computational Materials Engineering: A Transformational Discipline For Improved Competitiveness And National Security (The National Academies Press, 2008).
2. Tuegel, E. J., Ingraffea, A. R., Eason, T. G. & Spottswood, S. M. Reengineering aircraft structural life prediction using a digital twin. Int. J. Aerosp. Eng. 2011, 1–14 (2011).
3. Glaessgen, E. & Stargel, D. The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles. In 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference (American Institute of Aeronautics and Astronautics, 2012).
4. Tao, F. et al. Digital twin-driven product design, manufacturing and service with big data. Int. J. Adv. Manuf. Technol. 94, 3563–3576 (2017).
5. Boschert, S. & Rosen, R., Digital twin—the simulation aspect. In Mechatronic Futures, (eds Hehenberger, P. & Bradley, D.) 59–74 (Springer International Publishing, 2016).
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