Identifiability and amortized inference limitations in Kuramoto models
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 po...
| Authors | Emma Hannula, Jana de Wiljes, Matthew T. Moores et al. |
|---|---|
| Primary category | stat.AP |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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