The journal focuses on uncertainty quantification methods for complex systems. This includes developing and applying techniques for nonlinear models, inverse problems, and computationally expensive simulations. Research covers areas such as Bayesian inference, Gaussian processes, adaptive sampling, and optimal experimental design to improve predictions and reduce computational costs.
Applied MathematicsStatistics, Probability and UncertaintyDiscrete Mathematics and CombinatoricsStatistics and ProbabilityModeling and Simulation
Research Topics (OpenAlex)
Probabilistic and Robust Engineering DesignAdvanced Multi-Objective Optimization AlgorithmsGaussian Processes and Bayesian InferenceModel Reduction and Neural NetworksMarkov Chains and Monte Carlo MethodsStatistical Methods and InferenceAdvanced Numerical Methods in Computational MathematicsOptimal Experimental Design MethodsNumerical methods in inverse problemsMathematical Approximation and Integration