Sensitivity analysis of inverted model parameters from transient electromagnetic measurements affected by induced polarization effects

We investigate the application of the distance-based global sensitivity analysis (DGSA) to evaluate the sensitivity of electrical model parameters obtained from transient electromagnetic (TEM) data including induced polarization (IP) effects. We propose novel open-source forward modeling and inversion routines for single-loop TEM data...

Extreme-oriented sensitivity analysis using sparse polynomial chaos expansion: Application to train–track–bridge systems

The use of sensitivity analysis is essential in model development for the purposes of calibration, verification, factor prioritization, and mechanism reduction. While most contributions to sensitivity methods focus on the average model response, this paper proposes a new sensitivity method focusing on the extreme response and structural limit...

Parameter space exploration for the probabilistic damage stability method for dry cargo ships

The prediction of the statutory attained subdivision index is a challenging issue for the initial design of ships due to the design freedom offered by a probabilistic damage stability assessment. To this end, optimisation techniques integrated with a parametric model of the internal layout may generate a preliminary subdivision design,...

Practical experience of sensitivity analysis: Comparing six methods, on three hydrological models, with three performance criteria

Currently, practically no modeling study is expected to be carried out without some form of Sensitivity Analysis (SA). At the same time, there is a large number of various methods and it is not always easy for practitioners to choose one. The aim of this paper is to briefly review main classes of SA methods, and to present the results of the...

Tree-based ensemble methods for sensitivity analysis of environmental models: A performance comparison with Sobol and Morris techniques

Complex environmental models typically require global sensitivity analysis (GSA) to account for non-linearities and parametric interactions. However, variance-based GSA is highly computationally expensive. While different screening methods can estimate GSA results, these techniques typically impose restrictions on sampling methods and input...