To simulate accelerative loading events using finite element, occupant-positioned human body models (HBMs) a settling process is performed to fully couple models to a seat. Traditionally, this method is performed by loading the model into a seat over a prescribed period of time. While there is no industry standard detailing how long models should be settled for, it is generally recommended to settle for ~400-1200ms to achieve a proper HBM-seat coupling [1]. Depending on computational resources, simulations of this size could take 4+ hours to run. The Least-squares Algorithm for Tracking Topology in Computational Elements (LATTICE) method has been created to substantially cut down this simulation time by interpolating between known end-state nodal coordinates based on given parameters to approximate final HBM position. To train the least-squares regression model, 600 settling simulations using the GHBMC M50-OS v2.3 and a simplified seat varied the parameters: ordinate scaling of the seat foam stress-strain curve, seat foam density, gravity vector, contact friction, element type (hexahedral or tetrahedral), and model posture. Latin Hypercube Sampling using orthogonal sampling was used to ensure the subset of continuous parameterized values covered the entire space. Once HBMs were output using the LATTICE method, validation was performed through a quantitative comparison of predicted models vs. gravity loaded settled models in frontal loading conditions in driver, passenger, and reclined configurations. The results of this study showed that the LATTICE method reduced time to obtain end-state nodal coordinates for positioned HBMs by ~98% (240 minutes vs. 4 minutes). Additionally, when comparing the predicted settled model against the gravity loaded settled model in a frontal sled pulse, the three seating configurations received average CORA scores of HBM kinematics and kinetics between 0.87 and 0.91. This indicates that the LATTICE method is capable of drastically reducing settling time while maintaining model fidelity.