Remote laser welding is increasingly being adopted within the automotive industry due to its high production throughput at lower cost and flexibility, making the welding process much faster and more accurate. However, a leading challenge preventing its systematic uptake in the industry is the lack of efficient in-process monitoring and assuring high weld quality in the presence of process variability. Weld quality is generally assessed by measuring the key geometrical features of the melt pool such as penetration depth, interface width; and, both upper and bottom concavity which are directly correlated to static and fatigue performance. Existing solutions extract patterns from real-time data such as: plasma charge, acoustic or optical emissions measurements, etc. and integrate multivariate statistics and machine learning algorithms to estimate only a single key geometrical feature of the weld. For example, acoustic or optical emissions provide molten pool oscillation frequency, leading to penetration depth; the dimension of the molten pool obtained by visual sensing with high speed camera is correlated to interface width. The lack of comprehensive multiphysics models linking monitoring data and multiple welding process parameters (i.e. laser power, welding speed, and focal offset) with multiple key geometrical features underscores the limitations of the current methods toward delivering automatic in-process closed-loop quality control system. The multiphysics model should have capabilities for monitoring multiple key geometrical features; and, capabilities for on-the-fly process adjustment to guarantee high quality weld. This paper presents a novel analytical physics-driven simulation approach to monitor multiple key geometrical features. The developed model has the capability to be used for in-process monitoring of key geometrical features and, furthermore is a necessary enabler for the development of in-process closed-loop process adjustment applicable for remote laser welding. The proposed method is applicable for in-process monitoring of zinc coated steel in overlap joint configuration considering part-to-part gap. (C) 2017 Laser Institute of America.