Many surface treatment processes involve some sort of energy transfer to the treated part. This transfer can cause thermal stresses due to temperature gradients and ultimately deteriorate the treated part. To reduce the effects from energy deposition, it is necessary to monitor the state of the part which is nontrivial due to “hidden” states that cannot be monitored, such as internal temperatures and thermal stresses. The project aims to couple an IR camera reading the part’s surface temperature with thermomechanical simulation to infer the hidden states. Ultimately, the simulation’s output will be used to determine where the camera should be repositioned for more accurate state computation. Thus, the simulation must be performed in real time to keep up with incoming IR camera data. A multitude of simulation methods will be explored, such as the material point method (MPM) and lattice-Boltzmann method (LBM). These solution methods will be coupled with other processes such as machine learning (ML) to speed up the solver and Kalman filters for data assimilation as the computer receives more IR images over time.