Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Dienstbier, Jana"'
Autor:
Kuchlbauer, Martina, Dienstbier, Jana, Muneer, Adeel, Hedges, Hanna, Stingl, Michael, Liers, Frauke, Pflug, Lukas
In this work, we propose a robust optimization approach to mitigate the impact of uncertainties in particle precipitation. Our model incorporates partial differential equations, more particular nonlinear and nonlocal population balance equations to d
Externí odkaz:
http://arxiv.org/abs/2306.15432
In this work, we present algorithmically tractable safe approximations of distributionally robust optimization (DRO) problems. The considered ambiguity sets can exploit information on moments as well as confidence sets. Typically, reformulation appro
Externí odkaz:
http://arxiv.org/abs/2301.11185
Autor:
Kuchlbauer, Martina, Dienstbier, Jana, Muneer, Adeel, Hedges, Hanna, Stingl, Michael, Liers, Frauke, Pflug, Lukas
Publikováno v:
In Computers and Chemical Engineering April 2024 183
Autor:
Dienstbier, Jana, Aigner, Kevin-Martin, Rolfes, Jan, Peukert, Wolfgang, Segets, Doris, Pflug, Lukas, Liers, Frauke
Publikováno v:
In Computers and Chemical Engineering January 2022 157
Autor:
Frank, Uwe1 (AUTHOR), Dienstbier, Jana2 (AUTHOR), Tischer, Florentin1 (AUTHOR), Wawra, Simon E.1 (AUTHOR), Gromotka, Lukas1 (AUTHOR), Walter, Johannes1,3 (AUTHOR), Liers, Frauke2 (AUTHOR), Peukert, Wolfgang1,3 (AUTHOR) wolfgang.peukert@fau.de
Publikováno v:
Separations (2297-8739). Apr2023, Vol. 10 Issue 4, p252. 23p.
Publikováno v:
In Computers and Chemical Engineering 6 April 2020 135
In this work, we present algorithmically tractable reformulations of distributionally robust optimization (DRO) problems. The considered ambiguity sets can exploit information on moments as well as confidence sets. Typically, reformulation approaches
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c1134fc9faee1095bf4e05c368be9c78
http://arxiv.org/abs/2301.11185
http://arxiv.org/abs/2301.11185