arXiv:2611.05031 · v1
Comparing model representations and fitting methods for port-Hamiltonian system identification
摘要
Port-Hamiltonian models describe physical systems by separating stored energy, dissipation and external inputs. Learning such models from data requires a choice of representation and fitting method. We compare using neural networks, analytic expressions and combinations of the two for representing the different components of the port-Hamiltonian system, and compare fitting the models with gradient-based optimisation or sparse regression. Testing on simulations of mechanical and electrical systems, with and without added noise, we present prediction accuracy and computational cost of the different approaches.
作者备注：6 pages, 3 figures; submitted to the invited session "Learning and identification of port-Hamiltonian systems" at the 21st IFAC Symposium on System Identification (SYSID 2027)