🤖 AI Summary
This study addresses the challenge of parameter identification in stochastic systems with mixed noise, where intractable likelihoods hinder conventional estimation. We propose Penn-GMD, a network that maps trajectories to full-covariance Gaussian mixture distributions and employs surjective parameterization with negative log-likelihood minimization to approximate the true likelihood. The core contribution lies in leveraging full covariance matrices to explicitly reveal parameter coupling and multimodal structures. This approach not only accurately recovers the underlying likelihood distribution but also naturally diagnoses unidentifiability. Consequently, Penn-GMD effectively resolves persistent difficulties in parameter estimation and uncertainty quantification for complex stochastic systems where traditional methods typically fail, offering a robust framework for handling intractable inference problems in noisy dynamical environments.
📝 Abstract
Parameter identification of stochastic dynamical systems driven by mixed noises is challenging due to intractable likelihood functions. We propose PENN-GMD, a parameter estimation neural network that maps partially observed trajectories to a Gaussian mixture distribution (GMD) over the system parameters. Unlike conventional uncertainty estimates, the GMD employs full covariance matrices to explicitly reveal parameter couplings and multi-modal likelihood structures. The network is trained by minimizing the negative log-likelihood via a surjective parameterization that hard-encodes all GMD constraints, thereby approximating the true likelihood. We validate the method on five numerical examples with increasing complexity, including systems driven by fractional Gaussian and Lévy noises, oscillators with colored noise, coupled neurons under different observability, and an aeroelastic airfoil with unidentifiable stochastic disturbances. Results demonstrate that PENN-GMD accurately recovers likelihood distributions, captures parameter couplings, and naturally diagnoses non-identifiability through variance broadening or mode splitting. These capabilities establish PENN-GMD as a practical tool for uncertainty-aware parameter identification in complex stochastic systems where conventional likelihood-based methods are infeasible.