In micro-electro-mechanical systems (MEMS) testing high overall precision and reliability are essential. Due to the additional requirement of runtime efficiency, machine learning methods have been investigated in recent years. However, these methods are often associated with inherent challenges concerning uncertainty quantification and guarantees of reliability. The goal of this paper is therefore to present a new machine learning approach in MEMS testing based on Bayesian inference to determine whether the estimation is trustworthy. The overall predictive performance as well as the uncertainty quantification are evaluated with four methods: Bayesian neural network, mixture density network, probabilistic Bayesian neural network and BayesFlow. They are investigated under the variation in training set size, different additive noise levels, and an out-of-distribution condition, namely the variation in the damping factor of the MEMS device. Furthermore, epistemic and aleatoric uncertainties are evaluated and discussed to encourage thorough inspection of models before deployment striving for reliable and efficient parameter estimation during final module testing of MEMS devices. BayesFlow consistently outperformed the other methods in terms of the predictive performance. As the probabilistic Bayesian neural network enables the distinction between epistemic and aleatoric uncertainty, their share of the total uncertainty has been intensively studied.

Calibration of complex system models with a large number of parameters using standard optimization methods is often extremely time-consuming and not fully automated due to the reliance on all-inclusive expert knowledge. We propose a sensitivity-guided iterative parameter identification and data generation algorithm. The sensitivity analysis replaces manual intervention, the parameter identification is realized by BayesFlow allowing for uncertainty quantification, and the data generation with the physics-enhanced latent space variational autoencoder (PELS-VAE) between two iteration steps enables inference of weakly identifiable parameters. A complete calibration experiment was conducted on a thermal model of an automotive cabin. The average relative error rate over all parameter estimates of 1.62% and the mean absolute error of calibrated model outputs of $$0.108\,^{\circ }$$0.108 ∘ C validate the feasibility and effectiveness of the method. Moreover, the entire procedure accelerates up to 1 day, whereas the classical calibration method takes more than 1 week.

This research paper summarizes the methods for causality inference from observational data and proposes the generalization ability of causality inference methods in machine learning. The paper is insightful for further development of machine learning...

Reliable quantitative phase analysis (QPA) of mineral residues---such as bottom ash from waste incineration and metallurgical slags---is essential for resource recovery and process control, yet traditional methods such as Rietveld refinement are labor-intensive and sensitive to user expertise. Deep learning offers a route to automate XRD-based QPA, but prior architectural comparisons of convolutional neural networks (CNNs) and Vision Transformers (ViTs) have treated phase classification and fraction regression as separate stages; a joint multi-task formulation with direct, controlled architectural comparison has not been reported. We train a single multi-task model that simultaneously classifies phase presence and regresses phase fractions on mixed-phase XRD patterns, and compare a 1D-CNN against a ViT under identical conditions using a large simulation dataset of patterns spanning seven crystalline phases and a small set of real laboratory measurements of four reference materials. Four evaluation scenarios---in-domain simulated, in-domain real, zero-shot transfer, and transfer learning with fine-tuning---explicitly quantify the synthetic-to-real domain gap. Both architectures achieve near-perfect classification and regression on simulated data. On real measurements, the ViT outperforms the CNN overall, reducing RMSE by 31\% and MAE by 35\%, while the CNN retains an advantage for phases with sharp, well-resolved peaks and transfers more robustly in the zero-shot setting. Fine-tuning on a small set of real labeled measurements largely closes the domain gap for both architectures, confirming the practical viability of the simulation-to-real transfer pipeline for automated mineral QPA.

Calibration of complex system models with a large number of parameters using standard optimization methods is often extremely time-consuming and not fully automated due to the reliance on all-inclusive expert knowledge. We propose a sensitivity-guided iterative parameter identification and data generation algorithm. The sensitivity analysis replaces manual intervention, the parameter identification is realized by BayesFlow allowing for uncertainty quantification, and the data generation with the Physics-Enhanced Latent Space Variational Autoencoder (PELS-VAE) between two iteration steps enables inference of weakly identifiable parameters. A complete calibration experiment was conducted on a thermal model of an automotive cabin. The average relative error rate over all parameter estimations of 1.62% and the mean absolute error of calibrated model outputs of 0.108 ◦C validate the feasibility and effectiveness of the method. Moreover, the entire procedure accelerates up to one day, whereas the classical calibration method takes more than one week.