The training of a machine learning model solely on experimental data, encompassing both pre- and post-process information, can reveal the general relationship of the directed energy deposition process. However, models trained in this manner encounter limitations in capturing critical in-process information occurring during deposition. This paper details the training of a deep learning model through the integration of in-process physics-based simulation information and a pre-process experiment dataset. The sulfur content of stainless steel 316L was selected as critical in-process information affecting the final track geometry and was captured using computational fluid dynamics simulation of a single-track deposition process, which cannot be captured accurately through experimentation. The physics-based simulation dataset was generated by obtaining the contour of deposition and dilution of the solidified track cross-section. The experiment was conducted using central composite design, and data augmentation was achieved through curve fitting using a response surface methodology regression model. Statistical analysis assessing the quality of simulation and experiment data was conducted. Among six baseline models, a deep learning model with a specified training sequence of experiment and simulation data, denoted as DL-AugExp-Sim-Exp, exhibited the best-performing R2 and root mean square error prediction accuracy for cross-section track shape. Notably, deep learning models trained with both experiment and simulation information demonstrated a lower R2 value compared to models trained solely with experiment data, revealing a tradeoff between R2 value and additional prediction capability. In summary, in this study, the integration of a physics-based simulation dataset demonstrated the additional prediction capability concerning the effect of sulfur content on track geometry.