Abdolazizi, Kian PhilippKian PhilippAbdolaziziLinka, KevinKevinLinkaSprenger, JohannaJohannaSprengerNeidhardt, MaximilianMaximilianNeidhardtSchlaefer, AlexanderAlexanderSchlaeferCyron, Christian J.Christian J.Cyron2021-07-092021-07-092021-01-25Proceedings in applied mathematics and mechanics 20 (1): 202000284 (2021)http://hdl.handle.net/11420/9856Gelatin phantoms are frequently used in the development of surgical devices and medical imaging techniques. They exhibit mechanical properties similar to soft biological tissues [1] but can be handled at a much lower cost. Moreover, they enable a better reproducibility of experiments. Accurate constitutive models for gelatin are therefore of great interest for biomedical engineering. In particular it is important to capture the dependence of mechanical properties of gelatin on its concentration. Herein we propose a simple machine learning approach to this end. It uses artificial neural networks (ANN) for learning from indentation data the relation between the concentration of ballistic gelatin and the resulting mechanical properties. © 2021 The Authors Proceedings in Applied Mathematics & Mechanics published by Wiley-VCH GmbHen1617-706120211Wiley-VCHhttps://creativecommons.org/licenses/by/4.0/Biowissenschaften, BiologieTechnikMedizinIngenieurwissenschaftenConference Paper10.15480/882.389710.1002/pamm.20200028410.15480/882.3897Conference Paper