Metal Laser Powder Bed Fusion (PBF-LB/M) enables the manufacturing of complex geometric parts with significantly lower buy-to-fly ratio and shows promise in the aerospace industry. However, the widespread adoption of the technology remains constrained by the lack of process control in the absence of comprehensive understanding of the process physics and fast computational tools. In this work, we discuss physics-based multiphysics modeling framework for PBF-LB/M that combines high-fidelity simulations with calibrated reduced-order heat source models to enable both accurate and computationally efficient predictions. High-fidelity simulations are performed using the open-source finite-volume solver laserbeamFoam, developed within OpenFOAM [1]. The solver resolves coupled heat transfer, fluid flow, recoil pressure, and vaporization to capture three-dimensional melt pool evolution, and keyhole evolution. This simulation tool is well validated against measurements from the NIST Additive Manufacturing Benchmarks (2022 and 2025), demonstrating excellent agreement in melt pool depth, width, cooling rates, time above melting, and keyhole morphology across a range of laser powers and scan speeds. In parallel, an in-house-developed C++ finite-volume-based CFD framework employed cylindrical and conical volumetric heat sources for calibration and rapid prediction, scaled by volumetric energy density (relating scan speed, spot diameter, laser power, and laser absorption). This approach successfully predicted melt pool geometry, liquid and solid cooling rates, and time-above-melt as recognized through the 2022 [2] and 2025 NIST awards. While cylindrical heat sources offer minimal parameterization and rapid calibration, conical heat sources provide superior accuracy for multi-track overlap predictions. Comparisons with experiments and high-fidelity simulations highlight the trade-off between accuracy and computational cost, underscoring the value of calibrated heat source models for fast, part-scale prediction and digital-twin-enabled process optimization [3] in metal additive manufacturing. 1. Flint et. al. SoftwareX (2023) DOI: 10.1016/j.softx.2022.101299. 2. Amin et al. npj Comp Mat 10.1, (2024) DOI: 10.1038/s41524-024-01198-6. 3. Li et al. Additive Manufacturing 87 (2024), DOI: 10.1016/j.addma.2024.10