Probabilistic Numeric SMC Sampling for Bayesian Nonlinear System Identification in Continuous Time

TL;DR

This paper introduces a probabilistic numerical approach integrated with SMC methods for Bayesian nonlinear system identification in continuous time, effectively capturing uncertainty from noisy data and numerical integration.

Contribution

It presents a novel probabilistic numerical method for solving ODEs within Bayesian system identification, improving uncertainty quantification in nonlinear dynamic models.

Findings

Efficiently identifies latent states and parameters from noisy data.

Produces posterior distributions reflecting uncertainty in parameters.

Integrates probabilistic ODE solutions with SMC for enhanced modeling.

Abstract

In engineering, accurately modeling nonlinear dynamic systems from data contaminated by noise is both essential and complex. Established Sequential Monte Carlo (SMC) methods, used for the Bayesian identification of these systems, facilitate the quantification of uncertainty in the parameter identification process. A significant challenge in this context is the numerical integration of continuous-time ordinary differential equations (ODEs), crucial for aligning theoretical models with discretely sampled data. This integration introduces additional numerical uncertainty, a factor that is often over looked. To address this issue, the field of probabilistic numerics combines numerical methods, such as numerical integration, with probabilistic modeling to offer a more comprehensive analysis of total uncertainty. By retaining the accuracy of classical deterministic methods, these probabilistic approaches offer a deeper understanding of the uncertainty inherent in the inference process. This paper demonstrates the application of a probabilistic numerical method for solving ODEs in the joint parameter-state identification of nonlinear dynamic systems. The presented approach efficiently identifies latent states and system parameters from noisy measurements. Simultaneously incorporating probabilistic solutions to the ODE in the identification challenge. The methodology's primary advantage lies in its capability to produce posterior distributions over system parameters, thereby representing the inherent uncertainties in both the data and the identification process.