[2603.21752] Identifiability and amortized inference limitations in Kuramoto models

Statistics > Applications arXiv:2603.21752 (stat) [Submitted on 23 Mar 2026] Title:Identifiability and amortized inference limitations in Kuramoto models Authors:Emma Hannula, Jana de Wiljes, Matthew T. Moores, Heikki Haario, Lassi Roininen View a PDF of the paper titled Identifiability and amortized inference limitations in Kuramoto models, by Emma Hannula and 4 other authors View PDF HTML (experimental) Abstract:Bayesian inference is a powerful tool for parameter estimation and uncertainty quantification in dynamical systems. However, for nonlinear oscillator networks such as Kuramoto models, widely used to study synchronization phenomena in physics, biology, and engineering, inference is often computationally prohibitive due to high-dimensional state spaces and intractable likelihood functions. We present an amortized Bayesian inference approach that learns a neural approximation of the posterior from simulated phase dynamics, enabling fast, scalable inference without repeated sampling or optimization. Applied to synthetic Kuramoto networks, the method shows promising results in approximating posterior distributions and capturing uncertainty, with computational savings compared to traditional Bayesian techniques. These findings suggest that amortized inference is a practical and flexible framework for uncertainty-aware analysis of oscillator networks. Subjects: Applications (stat.AP); Machine Learning (cs.LG) Cite as: arXiv:2603.21752 [stat.AP]   (or arXiv:2603.21752v1 ...