A new model updating strategy with physics-based and data-driven models

Abstract

For engineering simulation models, insufficient experimental data and imperfect understanding of underlying physical principles often make predictive models inaccurate. It is difficult to reduce the model bias effectively with limited information. To improve the predictive performances of the models, this paper proposes a new model updating strategy utilizing a data-driven model to integrate with a physics-based model. One of the main strengths of the proposed method is that it maximizes the utilization of existing limited information by combining physics-based and data-driven models built based on different principles. First, the physics-based model is updated via selecting a suitable updating method and updating formulation. A data-driven model is then constructed using the Gaussian process (GP) regression. Finally, a weight combination is employed to obtain the updated predictive model where the weights of experimental sites and non-experimental sites are determined by the minimum discrepancy of probability distributions of the posterior error and another data-driven model, respectively. The Sandia thermal challenge problem is used to demonstrate the effectiveness of the proposed method.

Model Updating Based on Bayesian Theory and Improved Objective Function

“…To improve computational efficiency and reduce the cost of computation, meta-model methods have been developed. The meta-model methods obtain relatively simple proxy models by fitting the sample data of the FE model, such as the optimal polynomial response surface model [11], the Polynomial-chaotic Kriging (PCK) [12], the vectorial surrogate modeling (VSM) approach [13], the Gaussian process (GP) regression [14], the adaptive metamodel [15], etc. However, the accuracy of meta-model methods is relatively low and highly related to the determination of sample space.…”