Accurate identification and modeling of process maps in additive manufacturing remains a pertinent challenge. To ensure high quality and reliability of the finished product researchers, rely on models that entail the physics of the process as a computer code or conduct laboratory experiments, which are expensive and oftentimes demand significant logistic and overheads. Physics-based computational modeling has shown promise in alleviating the aforementioned challenge, albeit with limitations like physical approximations, model-form uncertainty, and limited experimental data. This calls for modeling methods that can combine limited experimental and simulation data in a computationally efficient manner, in order to achieve the desired properties in the manufactured parts. In this paper, we focus on demonstrating the impact of probabilistic modeling and uncertainty quantification on powder-bed fusion (PBF) additive manufacturing by focusing on the following three milieu: (a) accelerating the parameter development processes associated with laser powder bed fusion additive manufacturing process of metals, (b) quantifying uncertainty and identifying missing physical correlations in the computational model, and (c) transferring learned process maps from a source to a target process. These tasks demonstrate the application of multifidelity modeling, global sensitivity analysis, intelligent design of experiments, and deep transfer learning for a meso-scale meltpool model of the additive manufacturing process.