Sparse Grids for quantifying motion uncertainties in biomechanical models of radiotherapy patients

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SEEK ID: https://publications.h-its.org/publications/246

SEEK ID: https://publications.h-its.org/publications/246

DOI: 10.11588/emclpp.2017.01.35059

Research Groups: Data Mining and Uncertainty Quantification

Publication type: Journal

Journal: Preprint Series of the Engineering Mathematics and Computing Lab

Citation: Preprint Series of the Engineering Mathematics and Computing Lab, vol. 0(01)

Date Published: 2017

Registered Mode: imported from a bibtex file

Authors: Chen Song, Markus Stoll, Kristina Giske, Rolf Bendl, Vincent Heuveline

Citation
Song, C., Stoll, M., Giske, K., Bendl, R., & Heuveline, V. (2017). Sparse Grids for quantifying motion uncertainties in biomechanical models of radiotherapy patients. Preprint Series of the Engineering Mathematics and Computing Lab, No 01 (2017): Sparse Grids for quantifying motion uncertainties in biomechanical models of radiotherapy patients. https://doi.org/10.11588/EMCLPP.2017.01.35059
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Created: 7th Sep 2019 at 10:40

Last updated: 5th Mar 2024 at 21:23

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