Stochastic model calibration with image encoding: Converting high-dimensional sequential responses into RGB images for neural network inversion

Abstract

This paper proposes an inverse neural network approach for stochastic model calibration, focusing on the conversion of high-dimensional system sequential responses into RGB (Red, Green, and Blue) images, which significantly enhances the efficiency of calibration processes. By encoding multi-nodal, multi-directional data sequence into RGB images and employing advanced neural network architectures, including the Visual Geometry Group (VGG) network for frequency response data and Long Short-Term Memory (LSTM) integrated with Residual Networks (ResNet) for sequential time-domain data, the proposed method effectively decodes complex structural responses into stochastic model parameters. This process eliminates the need for conventional iterative optimization or Bayesian sampling methods, reducing computational costs while maintaining high accuracy in parameter identification. Two case studies, the NASA Langley Uncertainty Quantification Challenge and a satellite finite element model calibration task, demonstrate the effectiveness of the approach. The novel encoding–decoding framework enables real-time model calibration for high-dimensional data, making it a promising solution for complex engineering systems with large scale, high-dimensional data and inevitable uncertainties.