The computational cost of high-fidelity engineering simulations, for example CFD, is prohibitive if the application requires frequent design iterations or even fully fledged optimization. A popular way to reduce the computational cost and enable fast iteration cycles is to use surrogate models that are trained to predict simulation results from historical simulation data. While most traditional methods are parametric, ANNs are able to process geometries directly and are thus agnostic to the parametrization of the geometric models, which makes them appealing when working on multiple design campaigns. However, ANNs may fail to transfer the learned knowledge when used on new design campaigns that are significantly different from those used to train the model or when the size of the training data set is too small.

The goal of this project is to increase the reliability of ANN-based surrogate models on new design campaigns and on small datasets. One main direction to achieve this goal is to incorporate prior physical knowledge into the learning process. Methods to supplement training data with a simplified solution, meaningful physical scaling, and governing equation-based losses were used. The data set used for this study was based on a real-life-inspired complex 3D parametrized geometry of an automotive HVAC system.

The methods were tested in generalization, transfer learning, and single-campaign inference tasks. Moreover, physics-informed losses were tested in an ill-posed setting as a prediction outlier correction tool. While the initial findings suggest that incorporating prior physical knowledge may improve model performance, especially in low-data regimes, further investigation is needed to draw any definitive conclusions. Additionally, the preliminary results related to the use of physics-informed losses as a correction method are inconclusive and require further experimentation in the future. Therefore, more research is needed to determine the effectiveness of these approaches.