Morand, L.L.MorandHelm, D.D.Helm2022-03-0529.5.20212019https://publica.fraunhofer.de/handle/publica/25775910.24406/publica-r-25775910.1016/j.commatsci.2019.04.003To simulate the mechanical behavior of a material, it is essential to calibrate the internal parameters of the used material model to experimental measurements. This is typically done in a trail-and-error approach by hand or automatically using optimization algorithms. As an alternative to trial-and-error, neural network-based approaches can be used to imitate the inverse mapping. This is usually realized in a grey-box model, combining neural networks, deterministic models, and domain knowledge. However, the proposed neural network-based approaches found in literature do not address the challenge that is posed when the parameter identification problem is non-unique. In the present paper this problem is discussed and an improved approach is proposed using a mixture of experts model. Mixture of experts is an ensemble technique based on a dynamically structured framework of submodels aiming to partition the non-unique problem into unique subtasks.engrey-box modeldirect inverse modelinverse problemmachine learningmaterial modelingmixture of expertsneural networkparameter identification620A mixture of experts approach to handle ambiguities in parameter identification problems in material modelingjournal article