FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2788406418> ?p ?o ?g. }

• W2788406418 endingPage "095009" @default.
• W2788406418 startingPage "095009" @default.
• W2788406418 abstract "We develop a framework for goal-oriented optimal design of experiments (GOODE) for large-scale Bayesian linear inverse problems governed by PDEs. This framework differs from classical Bayesian optimal design of experiments (ODE) in the following sense: we seek experimental designs that minimize the posterior uncertainty in the experiment end-goal, e.g., a quantity of interest (QoI), rather than the estimated parameter itself. This is suitable for scenarios in which the solution of an inverse problem is an intermediate step and the estimated parameter is then used to compute a QoI. In such problems, a GOODE approach has two benefits: the designs can avoid wastage of experimental resources by a targeted collection of data, and the resulting design criteria are computationally easier to evaluate due to the often low-dimensionality of the QoIs. We present two modified design criteria, A-GOODE and D-GOODE, which are natural analogues of classical Bayesian A- and D-optimal criteria. We analyze the connections to other ODE criteria, and provide interpretations for the GOODE criteria by using tools from information theory. Then, we develop an efficient gradient-based optimization framework for solving the GOODE optimization problems. Additionally, we present comprehensive numerical experiments testing the various aspects of the presented approach. The driving application is the optimal placement of sensors to identify the source of contaminants in a diffusion and transport problem. We enforce sparsity of the sensor placements using an $ell_1$-norm penalty approach, and propose a practical strategy for specifying the associated penalty parameter." @default.
• W2788406418 created "2018-03-06" @default.
• W2788406418 creator A5037337131 @default.
• W2788406418 creator A5045561492 @default.
• W2788406418 creator A5082752382 @default.
• W2788406418 date "2018-07-26" @default.
• W2788406418 modified "2023-10-15" @default.
• W2788406418 title "Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems" @default.
• W2788406418 cites W1512208174 @default.
• W2788406418 cites W1575501007 @default.
• W2788406418 cites W1965555277 @default.
• W2788406418 cites W1968867201 @default.
• W2788406418 cites W1969549583 @default.
• W2788406418 cites W1983671004 @default.
• W2788406418 cites W1985666665 @default.
• W2788406418 cites W1994613299 @default.
• W2788406418 cites W1996810588 @default.
• W2788406418 cites W1997183438 @default.
• W2788406418 cites W2000359198 @default.
• W2788406418 cites W2001860888 @default.
• W2788406418 cites W2002544983 @default.
• W2788406418 cites W2005126631 @default.
• W2788406418 cites W2005140191 @default.
• W2788406418 cites W2014018052 @default.
• W2788406418 cites W2019143734 @default.
• W2788406418 cites W2019963142 @default.
• W2788406418 cites W2035814907 @default.
• W2788406418 cites W2042895566 @default.
• W2788406418 cites W205095828 @default.
• W2788406418 cites W2057424870 @default.
• W2788406418 cites W2057450058 @default.
• W2788406418 cites W2060442802 @default.
• W2788406418 cites W2074836377 @default.
• W2788406418 cites W2076580309 @default.
• W2788406418 cites W2077149349 @default.
• W2788406418 cites W2085499762 @default.
• W2788406418 cites W2096080920 @default.
• W2788406418 cites W2107133164 @default.
• W2788406418 cites W2133843658 @default.
• W2788406418 cites W2148323950 @default.
• W2788406418 cites W2157186153 @default.
• W2788406418 cites W2157234376 @default.
• W2788406418 cites W2226450186 @default.
• W2788406418 cites W2288973492 @default.
• W2788406418 cites W2291685518 @default.
• W2788406418 cites W2462634212 @default.
• W2788406418 cites W2560550287 @default.
• W2788406418 cites W2606342551 @default.
• W2788406418 cites W2619943112 @default.
• W2788406418 cites W2764128407 @default.
• W2788406418 cites W2962707560 @default.
• W2788406418 cites W2963128303 @default.
• W2788406418 cites W2963427446 @default.
• W2788406418 cites W2964065315 @default.
• W2788406418 cites W2964225090 @default.
• W2788406418 cites W2964324126 @default.
• W2788406418 cites W2964350355 @default.
• W2788406418 cites W340470818 @default.
• W2788406418 cites W4245654886 @default.
• W2788406418 doi "https://doi.org/10.1088/1361-6420/aad210" @default.
• W2788406418 hasPublicationYear "2018" @default.
• W2788406418 type Work @default.
• W2788406418 sameAs 2788406418 @default.
• W2788406418 citedByCount "23" @default.
• W2788406418 countsByYear W27884064182018 @default.
• W2788406418 countsByYear W27884064182019 @default.
• W2788406418 countsByYear W27884064182020 @default.
• W2788406418 countsByYear W27884064182021 @default.
• W2788406418 countsByYear W27884064182022 @default.
• W2788406418 countsByYear W27884064182023 @default.
• W2788406418 crossrefType "journal-article" @default.
• W2788406418 hasAuthorship W2788406418A5037337131 @default.
• W2788406418 hasAuthorship W2788406418A5045561492 @default.
• W2788406418 hasAuthorship W2788406418A5082752382 @default.
• W2788406418 hasBestOaLocation W27884064182 @default.
• W2788406418 hasConcept C105795698 @default.
• W2788406418 hasConcept C107673813 @default.
• W2788406418 hasConcept C111030470 @default.
• W2788406418 hasConcept C11413529 @default.
• W2788406418 hasConcept C121332964 @default.
• W2788406418 hasConcept C126255220 @default.
• W2788406418 hasConcept C134306372 @default.
• W2788406418 hasConcept C135252773 @default.
• W2788406418 hasConcept C17744445 @default.
• W2788406418 hasConcept C186394612 @default.
• W2788406418 hasConcept C191795146 @default.
• W2788406418 hasConcept C199539241 @default.
• W2788406418 hasConcept C207467116 @default.
• W2788406418 hasConcept C2524010 @default.
• W2788406418 hasConcept C2778755073 @default.
• W2788406418 hasConcept C28826006 @default.
• W2788406418 hasConcept C33923547 @default.
• W2788406418 hasConcept C34862557 @default.
• W2788406418 hasConcept C41008148 @default.
• W2788406418 hasConcept C62520636 @default.
• W2788406418 hasConceptScore W2788406418C105795698 @default.
• W2788406418 hasConceptScore W2788406418C107673813 @default.
• W2788406418 hasConceptScore W2788406418C111030470 @default.

• next