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[2602.11712] Potential-energy gating for robust state estimation in bistable stochastic systems
Summary
This article presents a novel method called potential-energy gating for robust state estimation in bistable stochastic systems, enhancing performance in noisy environments.
Why It Matters
The research addresses challenges in state estimation under uncertainty, particularly in non-ergodic settings where traditional statistical methods may fail. By leveraging a physics-based approach, it offers a significant improvement in accuracy, which is crucial for applications in various scientific fields.
Key Takeaways
- Potential-energy gating improves state estimation in bistable systems.
- The method outperforms standard filters, achieving 57-80% RMSE improvement.
- Robustness to parameter misspecification is demonstrated, maintaining improvements even with significant errors.
- The approach is particularly beneficial in data-scarce environments.
- Empirical applications include analysis of historical climate data.
Computer Science > Machine Learning arXiv:2602.11712 (cs) [Submitted on 12 Feb 2026 (v1), last revised 14 Feb 2026 (this version, v2)] Title:Potential-energy gating for robust state estimation in bistable stochastic systems Authors:Luigi Simeone View a PDF of the paper titled Potential-energy gating for robust state estimation in bistable stochastic systems, by Luigi Simeone View PDF HTML (experimental) Abstract:We introduce potential-energy gating, a method for robust state estimation in systems governed by double-well stochastic dynamics. The observation noise covariance of a Bayesian filter is modulated by the local value of a known or assumed potential energy function: observations are trusted when the state is near a potential minimum and progressively discounted as it approaches the barrier separating metastable wells. This physics-based mechanism differs from statistical robust filters, which treat all state-space regions identically, and from constrained filters, which bound states rather than modulating observation trust. The approach is especially relevant in non-ergodic or data-scarce settings where only a single realization is available and statistical methods alone cannot learn the noise structure. We implement gating within Extended, Unscented, Ensemble, and Adaptive Kalman filters and particle filters, requiring only two additional hyperparameters. Monte Carlo benchmarks (100 replications) on a Ginzburg-Landau double-well with 10% outlier contamination show ...
