The paper deals with inverse identification of mechanical parameters of gelatin gels. The material model is chosen a priori (Ogden) and although general success is reported in terms of correct identification of the two parameters of this model, it is highlighted for which cases this method and material model fail to characterise the gels. I believe the paper would be of interest to the readers of Soft Materials. Some suggestions for improving the paper are listed below:Authors thank the Reviewer's comments because they greatly help to improve the manuscript.1) English can be improved in several places. Also I am not certain the term 're-identified' parameters is the right term to use throughout the script. How about 'inversely predicted' parameters?English grammar was corrected. Changes were highlighted in red color in the revised version.Regarding the use of the "re-identified" term, we give here a brief explanation and mention the changes introduced in the manuscript to clarify this issue:In this work, we called "identified parameters" to those that are obtained by the inverse analysis using physical indentation curves as input data (ie. using experimental force and displacement measurements). In these cases, the values of the Ogden parameters that we want to identify by inverse analysis are unknown. On the other hand, we called re-identified parameters to those that are recovered by inverse analysis using a simulated curve as input data. In these cases, we know the actual values of the Ogden parameters that have to be identified because we use them to generate the force-displacement curve. The initial guesses in the inverse analysis are assumed 0.5 and 1.5 times the known parameters values. This procedure, in which virtual experimental data calculated by numerical simulations with chosen parameters replace the real experimental measurements, is often called parameter re-identification and it was used for example by other authors in: -Rauchs, G.: Optimization-based material parameter identification in indentation testing for finite strain elastoplasticity.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.