Deep learning-based surrogate modeling via physics-informed artificial image (PiAI) for strongly coupled multidisciplinary engineering systems

Abstract

The design of strongly coupled multidisciplinary engineering systems is challenging since it is characterized by the complex interaction of different disciplines. Such complexity cannot be easily captured by explicit analytical solutions, which motivates the development of a surrogate model. Deep learning (DL) has gained considerable interest among existing surrogate modeling techniques because of the flexible non-linear formulation, comparability to data format diversity, and applicability to data-driven analysis. Notably, a convolution neural network (CNN) has been employed in multifold research to ameliorate the prediction accuracy of the surrogate model once images representing physical phenomena are utilized. Nevertheless, it is still questionable to guarantee the feasibility of the CNN-based surrogate model in the multi-physics domain due to (1) unreliable correlation representation between multi-domain design parameters and the coupled responses and (2) massive training costs.To address those issues, therefore, this research proposes a framework of CNN-based deep surrogate model (DSM), developing a novel input structure called physics-informed artificial image (PiAI). PiAI incubates (1) geometry-informed CAD representing physical uncertainties of engineering systems, (2) location-clarified filter improving CNN training accuracy, and (3) simulation conditions essentially required in the multi-physics analysis, which reinforces the prediction credibility. Moreover, in lieu of employing multi-modalities or multiple image channels, the proposed method applies unimodal-based single image inputs to escalate computational efficiency. The proposed framework’s efficacy and applicability are addressed in practical engineering design applications: a cantilever beam and a stretchable strain sensor.