Topical issue scientific machine learning (2/2)

Abstract

We already have illustrated in the first issue [1] of this series that the emerging field of scientific machine learning is penetrating traditional fields within scientific computing and beyond. The second issue in this series is also devoted to demonstrating this rapid change. In this part of our special issue of the GAMM Mitteilungen, we continue the presentation of contributions on the topic of scientific machine learning in the context of complex applications across the sciences and engineering. We are pleased that again four teams of authors have accepted our invitation and are now illustrating their insights into recent research highlights as well as pointing the reader to the relevant literature and software. The four papers in this second part of the special issue are: