###### Abstract  
Increasing product requirements in the mechanical engineering industry and efforts to reduce time-to-market demand highly accurate and resource-efficient finite element simulations. The required parameter calibration of the material models is becoming increasingly challenging with regard to the growing variety of available materials. Besides the classical iterative optimization-based parameter identification method, novel machine learning-based methods represent promising alternatives, especially in terms of efficiency. However, the machine learning algorithms, architectures, and settings significantly affect the resulting accuracy. This work presents a comparative study of different machine learning algorithms based on virtual datasets with varying settings for the direct inverse material parameter identification method. Multilayer perceptrons, convolutional neural networks, and Bayesian neural networks are compared; and their resulting prediction accuracies are investigated. Furthermore, advantages in material parameter identification by uncertainty quantification using the Bayesian probabilistic approach are examined and discussed. The results show increased prediction quality when using convolutional neural networks instead of multilayer perceptrons. The assessment of the aleatoric and epistemic uncertainties when using Bayesian neural networks also demonstrated advantages in evaluating the reliability of the predicted material parameters and their influences on the subsequent finite element simulations. 1 202202  
with meshless methods [5]. In contrast, [PERSON] et al. [2] used the virtual fields method with the gradient-based algorithm fmincon to solve the inverse problem for full field data. Recently, the applicability of machine-learning-based methods is increasingly investigated, which shows promising results [6; 7]. These methods mostly aim at reducing the number of computationally intensive calculations. This paper focuses on the direct inverse MPI method, in which a surrogate model (in this case a neural network) learns the relationship between FE simulation results and the corresponding material parameters [6; 8]. The overall goal of this procedure is to pass the experimental result curves as input to the NN and to predict the MPs which can best reproduce the experimentally determined material behavior [6]. The knowledge gained from the simulations is hence stored in terms of the parameters of the NN and can be repeatedly retrieved, allowing for exceedingly efficient reuse. [PERSON] et al. [9] first proposed the use of a feedforward artificial neural network (FFANN) to predict the MP of a material model. In recent years, the direct inverse method has been further investigated and applied to other materials. [PERSON] et al. [10] used this approach to identify the plastic deformation and failure properties of ductile steels. The simulated material behavior based on the ductile elastoplastic damage GTN-model of Gurson, Tvergaard, and Needleman and the load-displacement curve of a small punch test were used as NN input. Furthermore, [PERSON] et al. [11] successfully used the direct inverse method to predict the anisotropic Hill parameters of an elasto-plastic model. [PERSON] et al. [12] designed a ring tensile specimen with effective homogeneous stress and strain distribution along the hoop direction via shape optimization and used the direct inverse method to predict the material parameters of the yield curve. [PERSON] et al. [6] demonstrated the viability of the direct inverse approach for retrieving fast solutions in MPI, but highlighted the influence of stochastic noise in the experimental data and the potential mismatch between the simulation model and experiment, which can lead to poor results in parameter estimation. Quantifying unavoidable uncertainties and evaluating their impact on the reliability of the simulation of complex components are increasingly becoming focuses of MPI tasks [13].