title: Utility of a novel error-stepping method to improve gradient-based parameter identification by increasing the smoothness of the local objective surface: a case-study of pulmonary mechanics.
abstract: Accurate model parameter identification relies on accurate forward model simulations to guide convergence. However, some forward simulation methodologies lack the precision required to properly define the local objective surface and can cause failed parameter identification. The role of objective surface smoothness in identification of a pulmonary mechanics model was assessed using forward simulation from a novel error-stepping method and a proprietary Runge-Kutta method. The objective surfaces were compared via the identified parameter discrepancy generated in a Monte Carlo simulation and the local smoothness of the objective surfaces they generate. The error-stepping method generated significantly smoother error surfaces in each of the cases tested (p<0.0001) and more accurate model parameter estimates than the Runge-Kutta method in three of the four cases tested (p<0.0001) despite a 75% reduction in computational cost. Of note, parameter discrepancy in most cases was limited to a particular oblique plane, indicating a non-intuitive multi-parameter trade-off was occurring. The error-stepping method consistently improved or equalled the outcomes of the Runge-Kutta time-integration method for forward simulations of the pulmonary mechanics model. This study indicates that accurate parameter identification relies on accurate definition of the local objective function, and that parameter trade-off can occur on oblique planes resulting prematurely halted parameter convergence.
authors.affiliation: Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand. Electronic address: paul.docherty@canterbury.ac.nz.
authors.forename: Paul D
authors.initials: PD
authors.lastname: Docherty
authors.affiliation: Institute of Technical Medicine (ITEM), Furtwangen University, Villengen-Schwenningen, Germany.
authors.forename: Christoph
authors.initials: C
authors.lastname: Schranz
authors.affiliation: Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand.
authors.forename: J Geoffrey
authors.initials: JG
authors.lastname: Chase
authors.affiliation: Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand.
authors.forename: Yeong Shiong
authors.initials: YS
authors.lastname: Chiew
authors.affiliation: Institute of Technical Medicine (ITEM), Furtwangen University, Villengen-Schwenningen, Germany.
authors.forename: Knut
authors.initials: K
authors.lastname: Möller
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D008168:Lung / Q000502:physiology*; D008954:Models, Biological; D009010:Monte Carlo Method; D011312:Pressure; D011650:Pulmonary Alveoli / Q000502:physiology; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012984:Software; D013997:Time Factors
keywords: Forward simulation; Inverse problem; Pulmonary mechanics