###### Abstract
The use of Bayesian neural networks (BNN) has proven beneficial to account for aleatory and epistemic uncertainties and to counteract NNs tendency to overfit due to BNNs' inherent generalization ability [14; 15; 16; 17]. Thereby, the model returns not only one (or more) deterministic MP, but a distribution object from which, for example, statistics such as the standard deviation can be computed by sampling. [PERSON] et al. [18] demonstrated the integration of BNNs into the direct inverse MPI and presented the advantages using a simple linear elastic material model with an exponential softening function. Since the input data of the direct inverse MPI method are mostly force-displacement, force-time, or similar curves, and thus an ordered set of real values, this task can be seen as a kind of time series classification problem [19]. In this case, however, the input data are not assigned a class label which would be typical for classification but a continuous number (the material parameter), which is typical for regression. Within the time series classification, different 1D convolutional neural network (CNN) architectures have proven to achieve very high prediction accuracies in recent years [20; 21]. To further increase their prediction accuracy, data augmentation strategies can be used to increase the size of the training dataset [22; 23]. These include random transformation, such as applying random noise to the input data, and time series decomposition methods [24]. Aside from the specific NN algorithm and the input dataset that is available for training, the hyperparameters (HP) of the NN architecture represent a major influencing factor on the achievable accuracy of the direct inverse MPI [25; 26]. Since the choice of suitable machine learning (ML) algorithms, architectures, and settings for the direct inverse MPI method is very challenging, a comparative study of the achievable prediction accuracy is presented within this paper. Multilayer perceptrons (MLP) and convolutional and Bayesian neural networks were implemented in a self-developed Python framework, and their performances were compared based on different settingsand datasets. A self-programmed custom loss function (CLF) was also implemented in the framework. The used CNNs are based on custom network architectures and on established architectures from the literature. Using the library Kerastuner, hyperparameter optimizations (HPO) were performed. Additionally, the influences of applying different Gaussian noise components to the training data and empirical mode decomposition as data augmentation techniques were investigated. The data basis of the investigations was a virtually generated dataset derived from experimental investigations of an additively processed acrylonitrile butadiene styrene (ABS). To describe the highly nonlinear material behavior in the FE software LS-DYNA, the material card MAT_187_SAMP-1 (semi-analytical model for polymers) was used in combination with the failure model MAT_ADD_DAMAGE_GISSMO. Thus, the investigations aimed to further improve the overall prediction accuracy of the direct inverse MPI method and to simplify the choice of suitable architectures and settings for researchers and engineers. By varying different methods and settings, the resulting effects and influences were revealed and quantified in different runs. In Section 2, the methods and NN architectures required for understanding the studies are briefly explained, and the used experimental dataset and the computational studies are described. The results of the study are presented and compared in Section 3. Finally, in Section 4 the results are summarized, and an outlook on possible further investigations is given.