Advanced Uncertainty Modelling in Science & Engineering (AUMSE)

The AUMSE disseminates information of permanent interest in the areas of analysis, theory, modelling, reasoning, design and control of (complex) systems in the presence of uncertainty. The AUMSE seeks to emphasize methods that cross stochastic analysis, modelling or computing under imprecise/statistical uncertainty. Topics of particular interest include representation and identification of uncertainty, uncertainty propagation, (non)Bayesian stochastic PDEs/ODEs, data-driven approaches for constructing stochastic models, validation, verification, reasoning and uncertainty quantification for predictive computational science, and visualization of uncertainty in high-dimensional spaces. Bayesian and non-Bayesian computation and machine learning techniques are also of interest for example in the context of stochastic/uncertain multiscale systems, for model selection/classification, and decision making. Applications of uncertainty modelling and reasoning in all areas of physical and biological sciences & engineering as well as Industry 4.0 are appropriate. The area and domain of interest for AUMSE are as follows: Connections between Uncertainty Modelling and Machine Learning / Deep learning Uncertain or incomplete Data Optimization and optimal control under uncertainty Prediction/estimation under Uncertainty Rare events and Risk under uncertainty Remote Sensing and sensor data under uncertainty (non)Bayesian Stochastic models Uncertainty propagation Verification and validation under uncertainty Decision Theory under uncertaintyInvited lecture, invited by: CMMSE General Chairs, prof. Jesús Vigo Aguiarstatus: Published onlin