The paper concerns abdominal wall modelling. The accurate prediction and simulation of abdominal wall mechanics are important in the context of optimization of ventral hernia repair. The shell Finite Element model is considered, as the one which can be used in patient-specific approach due to relatively easy geometry generation. However, there are uncertainties in this issue, e.g. related to mechanical properties since the properties...

Global sensitivity analysis (GSA) is a method to quantify the effect of the input parameters on outputs of physics-based systems. Performing GSA can be challenging due to the combined effect of the high computational cost of each individual physics-based model, a large number of input parameters, and the need to perform repetitive model evaluations. To reduce this cost, neural networks (NNs) are used to replace the expensive physics-based...

The work presents testing methods of sensitivity and reliability of mechanical or structural systems. All computations concerned the case of Zigler column, a simple model of a compressed column involving two random variables only. A conclusion was drawn that the standard Monte Carlo method, its reduction variants and the response surface method allow to assess the sensitivity of structural response to the variation of random structural...

The paper addresses the issue of ventral hernia repair. Finite Element simulations can be helpful in the optimization of hernia parameters. A membrane abdominal wall model is proposed in two variants: a healthy one and including hernia defect repaired by implant. The models include many uncertainties, e.g. due to variability of abdominal wall, intraabdominal pressure value etc. Measuring mechanical properties with high accuracy...

Performing global sensitivity analysis (GSA) can be challenging due to the combined effect of the high computational cost, but it is also essential for engineering decision making. To reduce this cost, surrogate modeling such as neural networks (NNs) are used to replace the expensive simulation model in the GSA process, which introduces the additional challenge of finding the minimum number of training data samples required to...