Aehle, Max; Alme, Johan; Gábor Barnaföldi, Gergely; Blühdorn, Johannes; Bodova, Tea; Borshchov, Vyacheslav; van den Brink, Anthony; Eikeland, Viljar Nilsen; Feofilov, Gregory; Garth, Christoph; Gauger, Nicolas R; Grøttvik, Ola Slettevoll; Helstrup, Håvard; Igolkin, Sergey; Keidel, Ralf; Kobdaj, Chinorat; Kortus, Tobias; Kusch, Lisa; Leonhardt, Viktor; Mehendale, Shruti Vineet; Ningappa Mulawade, Raju; Odland, Odd Harald; O'Neill, George; Papp, Gábor; Peitzmann, Thomas; Pettersen, Helge Egil Seime; Piersimoni, Pierluigi; Pochampalli, Rohit; Protsenko, Maksym; Rauch, Max Philip; Rehman, Attiq Ur; Richter, Matthias; Röhrich, Dieter Rudolf Christian; Sagebaum, Max; Santana, Joshua; Schilling, Alexander; Seco, Joao; Songmoolnak, Arnon; Sudár, Ákos; Tambave, Ganesh Jagannath; Tymchuk, Ihor; Ullaland, Kjetil; Varga-Kofarago, Monika; Volz, Lennart; Wagner, Boris; Wendzel, Steffen; Wiebel, Alexander; Xiao, Renzheng; Yang, Shiming; Zillien, Sebastian (Peer reviewed; Journal article, 2023)
Objective. Gradient-based optimization using algorithmic derivatives can be a useful technique to improve engineering designs with respect to a computer-implemented objective function. Likewise, uncertainty quantification ...