What question did this study set out to answer?

To develop a stable and scalable deep variational inference framework for joint latent-state estimation and parameter identification in nonlinear state-space models.

August 19, 2026International Journal of Network Dynamics and IntelligenceOpen Access

Deep Variational System Identification for Nonlinear State-Space Models via Alternating Optimization

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Authors

KFKaiqi FangHuazhong University of Science and Technology GMGuijun MaHuazhong University of Science and Technology YWYasen WangSouth China University of Technology

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Overview

Computational study demonstrates stable parameter estimation in nonlinear dynamical systems, suggesting improved identification of latent trajectories and noise.

Key Points

To develop a stable and scalable deep variational inference framework for joint latent-state estimation and parameter identification in nonlinear state-space models.
Parameterized the variational posterior mean using a non-causal dilated residual convolutional network while preserving a block-tridiagonal precision matrix for linear-time inference.
Introduced an alternating optimization strategy decoupling variational smoothing via an evidence lower bound approximation from analytic model and noise parameter updates.
Tested the framework on benchmark simulations comprising a nonlinear discrete-time system and a stochastic Duffing oscillator.
Eliminated optimization instabilities commonly associated with expressive deep parameterizations in joint state and parameter learning.
Achieved accurate and reliable estimation of nonlinear system dynamics alongside underlying noise statistics.

Cite This Study

Fang et al. (2026) studied this question.

synapsesocial.com/papers/6a8563d703308d306e2d7428 https://doi.org/10.53941/ijndi.2026.100017

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August 19, 2026International Journal of Network Dynamics and IntelligenceOpen Access

Deep Variational System Identification for Nonlinear State-Space Models via Alternating Optimization

View Full Paper

Ask AI

Bookmark

Authors

KFKaiqi FangHuazhong University of Science and Technology GMGuijun MaHuazhong University of Science and Technology YWYasen WangSouth China University of Technology

Discussion

Member takes

Overview

Computational study demonstrates stable parameter estimation in nonlinear dynamical systems, suggesting improved identification of latent trajectories and noise.

Key Points

To develop a stable and scalable deep variational inference framework for joint latent-state estimation and parameter identification in nonlinear state-space models.
Parameterized the variational posterior mean using a non-causal dilated residual convolutional network while preserving a block-tridiagonal precision matrix for linear-time inference.
Introduced an alternating optimization strategy decoupling variational smoothing via an evidence lower bound approximation from analytic model and noise parameter updates.
Tested the framework on benchmark simulations comprising a nonlinear discrete-time system and a stochastic Duffing oscillator.
Eliminated optimization instabilities commonly associated with expressive deep parameterizations in joint state and parameter learning.
Achieved accurate and reliable estimation of nonlinear system dynamics alongside underlying noise statistics.

Cite This Study

Fang et al. (2026) studied this question.

synapsesocial.com/papers/6a8563d703308d306e2d7428 https://doi.org/10.53941/ijndi.2026.100017

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