Numerical simulations for the analysis and design of structures or systems are often based on deterministic characteristics, whereas reality is determined by data information which characterized various types uncertainty (variability, imprecision, inaccuracy, incompleteness). Besides traditional probabilistic approaches, possibilistic models most recently in focal point research. Combining char...

Battery Cell design and control have been widely explored through modeling simulation. On the one hand, Doyle’s pseudo-two-dimensional (P2D) model Single Particle Models are among most popular electrochemical models capable of predicting battery performance therefore guiding cell characterization. other empirical obtained, for example, by Machine Learning (ML) methods represent a simpler comput...

In both numerical and stochastic optimization methods, surrogate models are often employed in lieu of the expensive high-fidelity models to enhance search efficiency. In gradient-based numerical methods, the trustworthiness of the surrogate models in predicting the fitness improvement is often addressed using ad hoc move limits or a trust region framework (TRF). Inspired by the success of TRF i...

In a randomized clinical trial, a statistic that measures the proportion of treatment effect on the primary clinical outcome that is explained by the treatment effect on a surrogate outcome is a useful concept. We investigate whether a statistic proposed to estimate this proportion can be given a causal interpretation as defined by models of counterfactual variables. For the situation of binary...

ion in this study area. Different groundwater extraction scenarios were generated using Latin hypercube sampling. The salinity concentrations resulting from each of these pumping patterns are simulated using FEMWATER. The simulated salinity level and the corresponding pumping rates form the input-output pattern. Altogether 230 extraction patterns are used in this study. Different realizations o...

In failure probability estimation, importance sampling constructs a biasing distribution that targets the failure event such that a small number of model evaluations is sufficient to achieve a Monte Carlo estimate of the failure probability with an acceptable accuracy; however, the construction of the biasing distribution often requires a large number of model evaluations, which can become comp...