Matches in SemOpenAlex for { <https://semopenalex.org/work/W3010443800> ?p ?o ?g. }
- W3010443800 abstract "Efficient uncertainty quantification algorithms are key to understand the propagation of uncertainty -- from uncertain input parameters to uncertain output quantities -- in high resolution mathematical models of brain physiology. Advanced Monte Carlo methods such as quasi Monte Carlo (QMC) and multilevel Monte Carlo (MLMC) have the potential to dramatically improve upon standard Monte Carlo (MC) methods, but their applicability and performance in biomedical applications is underexplored. In this paper, we design and apply QMC and MLMC methods to quantify uncertainty in a convection-diffusion model of tracer transport within the brain. We show that QMC outperforms standard MC simulations when the number of random inputs is small. MLMC considerably outperforms both QMC and standard MC methods and should therefore be preferred for brain transport models." @default.
- W3010443800 created "2020-03-13" @default.
- W3010443800 creator A5020092884 @default.
- W3010443800 creator A5048982998 @default.
- W3010443800 creator A5050052245 @default.
- W3010443800 date "2020-03-04" @default.
- W3010443800 modified "2023-09-27" @default.
- W3010443800 title "Fast uncertainty quantification of tracer distribution in the brain interstitial fluid with multilevel and quasi Monte Carlo" @default.
- W3010443800 cites W1492326914 @default.
- W3010443800 cites W1525132831 @default.
- W3010443800 cites W158756560 @default.
- W3010443800 cites W1599063302 @default.
- W3010443800 cites W1835030294 @default.
- W3010443800 cites W1837874438 @default.
- W3010443800 cites W1994458472 @default.
- W3010443800 cites W2014945091 @default.
- W3010443800 cites W2014987689 @default.
- W3010443800 cites W2017769677 @default.
- W3010443800 cites W2023196007 @default.
- W3010443800 cites W2024955728 @default.
- W3010443800 cites W2040880670 @default.
- W3010443800 cites W2075492136 @default.
- W3010443800 cites W2079321251 @default.
- W3010443800 cites W2079559649 @default.
- W3010443800 cites W2079884250 @default.
- W3010443800 cites W2079884585 @default.
- W3010443800 cites W2080198834 @default.
- W3010443800 cites W2087476813 @default.
- W3010443800 cites W2094168343 @default.
- W3010443800 cites W2106605796 @default.
- W3010443800 cites W2112311198 @default.
- W3010443800 cites W211905087 @default.
- W3010443800 cites W2119436275 @default.
- W3010443800 cites W2126980197 @default.
- W3010443800 cites W2128811293 @default.
- W3010443800 cites W2135290713 @default.
- W3010443800 cites W2135459060 @default.
- W3010443800 cites W2152719134 @default.
- W3010443800 cites W2163715525 @default.
- W3010443800 cites W2212370034 @default.
- W3010443800 cites W2298381282 @default.
- W3010443800 cites W2303654018 @default.
- W3010443800 cites W2319583746 @default.
- W3010443800 cites W2409658224 @default.
- W3010443800 cites W2414014505 @default.
- W3010443800 cites W2560579910 @default.
- W3010443800 cites W2563126553 @default.
- W3010443800 cites W2746870493 @default.
- W3010443800 cites W2747884879 @default.
- W3010443800 cites W2750130602 @default.
- W3010443800 cites W2766060569 @default.
- W3010443800 cites W2790584092 @default.
- W3010443800 cites W2801110912 @default.
- W3010443800 cites W2811395263 @default.
- W3010443800 cites W2837320447 @default.
- W3010443800 cites W2884775797 @default.
- W3010443800 cites W2891469086 @default.
- W3010443800 cites W2896165108 @default.
- W3010443800 cites W2901166225 @default.
- W3010443800 cites W2913113106 @default.
- W3010443800 cites W2921203162 @default.
- W3010443800 cites W2947462870 @default.
- W3010443800 cites W2947675618 @default.
- W3010443800 cites W2950960564 @default.
- W3010443800 cites W2963267705 @default.
- W3010443800 cites W2963334476 @default.
- W3010443800 cites W2964191339 @default.
- W3010443800 cites W2964218952 @default.
- W3010443800 cites W2964295897 @default.
- W3010443800 cites W2976029637 @default.
- W3010443800 cites W2982626951 @default.
- W3010443800 cites W3021722416 @default.
- W3010443800 cites W3036660908 @default.
- W3010443800 cites W311354260 @default.
- W3010443800 cites W3170938646 @default.
- W3010443800 cites W3208083106 @default.
- W3010443800 cites W39759813 @default.
- W3010443800 cites W43284170 @default.
- W3010443800 cites W644536862 @default.
- W3010443800 cites W73123583 @default.
- W3010443800 hasPublicationYear "2020" @default.
- W3010443800 type Work @default.
- W3010443800 sameAs 3010443800 @default.
- W3010443800 citedByCount "0" @default.
- W3010443800 crossrefType "posted-content" @default.
- W3010443800 hasAuthorship W3010443800A5020092884 @default.
- W3010443800 hasAuthorship W3010443800A5048982998 @default.
- W3010443800 hasAuthorship W3010443800A5050052245 @default.
- W3010443800 hasConcept C105795698 @default.
- W3010443800 hasConcept C111350023 @default.
- W3010443800 hasConcept C119857082 @default.
- W3010443800 hasConcept C121332964 @default.
- W3010443800 hasConcept C121864883 @default.
- W3010443800 hasConcept C126255220 @default.
- W3010443800 hasConcept C13153151 @default.
- W3010443800 hasConcept C19499675 @default.
- W3010443800 hasConcept C32230216 @default.
- W3010443800 hasConcept C33923547 @default.
- W3010443800 hasConcept C41008148 @default.
- W3010443800 hasConcept C63320529 @default.