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 surrogate modeling. It enables the prediction of the systems’ behavior without analytical formulations. Among existing surrogate modeling techniques, deep learning has gained significant interest because of the flexibility of non-linear formulation and applicability to data-driven analysis. Notably, the convolution neural networks-based deep surrogate model augments the precision of prediction and estimation of system behavior once image-based inputs representing physical experiments and simulation are employed.

Nevertheless, the feasibility of the deep surrogate model is often flawed due to the miserable correlation representation between design parameters and the corresponding responses. Massive training costs also degrade the performance of the predictive model. To address those issues, this research proposes a physics-informed artificial image (PiAI) that incubates geometry-informed CAD, location-clarified filter, and essential simulation conditions, which augments the prediction credibility. Moreover, in lieu of employing multimodalities or multiple image channels, the proposed method employs a unimodal-based single image input to increase computational efficiency. The proposed framework’s efficacy and applicability are addressed in practical engineering design applications: cantilever beam and stretchable strain sensor.

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