Similar papers
Open access
2026
A Bi-Stage Gaussian Process Framework for Modeling and Predicting Nonlinear Non-Autonomous Dynamics
A Bi-stage Gaussian Process (GP) framework for non-autonomous ODEs, capable of estimating system states and their derivatives directly from noisy measurements, providing a reliable alternative to classical ODE modeling in noisy and complex systems.
R. Fezai, Byanne Malluhi, Nour Basha et al.
· IEEE Access · 0 citations
Conference
Jul 2026
Low-Order Continuous-Time Koopman Operator Learning via Physics-Informed Neural Networks
One of the main objectives in control theory is to obtain a linear representation of inherently nonlinear systems in order to leverage the analytical and theoretical tools developed for linear systems. In this context, the Koopman operator has attracted increasing interest in recent years.Koopman operator theory provid...
M. Zodros, A. Colotti, M. Yagoubi et al.
· International Conference on... · 0 citations
Review
Open access
May 2026
Data-Driven Identification of Stochastic Dynamical Systems
Identifying stochastic dynamical systems from observational data remains a major challenge in applied mathematics and engineering, particularly when complex systems are influenced by random perturbations and incomplete empirical information. This comprehensive review aims to examine state-of-the-art data-driven methods...
Rishav Jha, Kameshwar Sahani, S. K. Sahani et al.
· African Multidisciplinary Jo... · 0 citations
Jul 2026
Gaussian behaviors and stochastic data-driven control
A stochastic behavioral modeling framework, termed Gaussian behaviors, which augments a deterministic linear time-invariant behavior with a Gaussian noise component is proposed, which enables simple and tractable stochastic data-driven control methods.
András Sasfi, A. Padoan, I. Markovsky et al.
· arXiv.org · 0 citations
Preprint
Aug 2026
Spectral Distillation: From Nonlinear Dynamics to Linear State-Space Models
This work first learns an implicit spectral predictor using Observation Spectral Filtering using Observation Spectral Filtering, a convex method that competes with the best linear observer for the system, and applies spectral-to-LDS distillation to convert this predictor into an explicit recurrent linear dynamical syst...
Liane Galanti, Devan Shah, Shlomo Fortgang et al.
· 0 citations
Open access
Aug 2026
An interpretable data-driven identification of dynamical systems via universal neural ordinary differential equations
An interpretable identification framework based on universal neural ordinary differential equations (UNODEs), symbolic regression, and parameter refinement is developed that is competitive on autonomous polynomial systems and more effective in recovering compact symbolic structures for non-polynomial and explicitly tim...
Qing-Tong Dong
· Engineering Research Express · 0 citations