2019/12/30 by Yu Chen, Chen, Yu, Jin Cheng +7
Engineering · #Building Energy and Comfort Optimization #Combustion and flame dynamics #FOS: Computer and information sciences #FOS: Mathematics #Fluid Dynamics and Turbulent Flows #Methodology (stat.ME) #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.1912.12783
openalex publication_date 2019/12/30 · openalex created_date 2020/01/10 · openalex updated_date 2026/07/28
Parameter inference of dynamical systems is a challenging task faced by many researchers and practitioners across various fields. In many applications, it is common that only limited variables are observable. In this paper, we propose a method for parameter inference of a system of nonlinear coupled ODEs with partial observations. Our method combines fast Gaussian process based gradient matching (FGPGM) and deterministic optimization algorithms. By using initial values obtained by Bayesian steps with low sampling numbers, our deterministic optimization algorithm is both accurate and efficient.