In manufacturing industry, finding optimal design parameters for targeted properties has traditionally been 

guided by trial and error. However, limited data availability to few hundreds sets of experimental data in typical 

materials processes, the machine-learning capabilities and other data-driven modeling (DDM) techniques are too 

far from it to be practical. In this study, we show how a versatile design strategy, tightly coupled with physics- 

based modeling (PBM) data, can be applied to small set of experimental data to improve the optimization of 

process parameters. Our strategy uses PBM to achieve augmented data that includes essential physics: in other 

words, the PBM data allows the inverse design model to ¡®learn¡¯ physics, indirectly. We demonstrated the ac- 

curacy of both forward-prediction and inverse-optimization have been dramatically improved with the help of 

PBM data, which are relatively cheap and abundant. Furthermore, we found that the inverse model with 

augmented data can accurately optimize process parameters, even for ones those were not considered in the 

simulation. Such versatile strategy can be helpful for processes/experiments for the cases where the number of 

collectable data is limited, which is most of the case in industries.