Identifying the Influential Inputs for Network Output Variance Using Sparse Polynomial Chaos Expansion

by   Zhanlin Liu, et al.

Sensitivity analysis (SA) is an important aspect of process automation. It often aims to identify the process inputs that influence the process output's variance significantly. Existing SA approaches typically consider the input-output relationship as a black-box and conduct extensive random sampling from the actual process or its high-fidelity simulation model to identify the influential inputs. In this paper, an alternate, novel approach is proposed using a sparse polynomial chaos expansion-based model for a class of input-output relationships represented as directed acyclic networks. The model exploits the relationship structure by recursively relating a network node to its direct predecessors to trace the output variance back to the inputs. It, thereby, estimates the Sobol indices, which measure the influence of each input on the output variance, accurately and efficiently. Theoretical analysis establishes the validity of the model as the prediction of the network output converges in probability to the true output under certain regularity conditions. Empirical evaluation on two manufacturing processes shows that the model estimates the Sobol indices accurately with far fewer observations than a state-of-the-art Monte Carlo sampling method.


Efficient estimation of divergence-based sensitivity indices with Gaussian process surrogates

We consider the estimation of sensitivity indices based on divergence me...

Data-Driven Sensitivity Indices for Models With Dependent Inputs Using the Polynomial Chaos Expansion

Uncertainties exist in both physics-based and data-driven models. Varian...

Efficient sampling for polynomial chaos-based uncertainty quantification and sensitivity analysis using weighted approximate Fekete points

Performing uncertainty quantification (UQ) and sensitivity analysis (SA)...

Sensitivity Analysis of High-Dimensional Models with Correlated Inputs

Sensitivity analysis is an important tool used in many domains of comput...

Global Sensitivity Analysis via Multi-Fidelity Polynomial Chaos Expansion

The presence of uncertainties are inevitable in engineering design and a...

Multifidelity conditional value-at-risk estimation by dimensionally decomposed generalized polynomial chaos-Kriging

We propose novel methods for Conditional Value-at-Risk (CVaR) estimation...

Please sign up or login with your details

Forgot password? Click here to reset