Regime-Switching Dynamical Systems

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1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Broadly Applicable Approximate MCMC for Switching Stochastic Differential Equations Using Uniformization and Time-Conditioned Factorized Neural Likelihood Estimation
Oct 7, 2026Shion Hosoda, Michiaki HamadaStochastic Differential Equations Regime-Switching Dynamical Systems
Waseda University · National Institute of Advanced Industrial Science and Technology · Nippon Medical School
Inferring Multi-Timescale Neural Dynamics with Switching Linear Dynamical Systems
Oct 1, 2026Lulu Gong, Yongxu Zhang, Shreya SaxenaStochastic Linear Dynamical Systems Latent Dynamics Modeling
Center for Neurocomputation and Machine Intelligence, Wu Tsai Institute, Department of Biomedical Engineering, Yale University, New Haven, CT 06510, USA
Indirect Geoeconomic Influence: A Switching Dynamical Systems Framework for Mechanism Design
Aug 8, 2026Nikolos Gurney, Boxi Fu, Soham Hans +1Mechanism Design Regime-Switching Dynamical Systems
Institute for Creative Technologies, University of Southern California
Self-Adaptive Learning and Model Predictive Control for Tracking Unknown Dynamics with No Regret
Jul 29, 2026Atharva Navsalkar, Hongyu Zhou, Vasileios TzoumasDynamical Systems Regime-Switching Dynamical Systems
Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109 USA
Learning switched non-linear dynamical systems from a single trajectory
Jul 26, 2026Sunny G. W. Wang, Hemant TyagiRegime-Switching Dynamical Systems Nonlinear System Identification
Division of Mathematical Sciences, SPMS, NTU Singapore 637371
Reasoning Fine-Tuning Induces Persistent Latent Policy States
Jul 20, 2026Abir Harrasse, Michael Lan, Hunar Batra +2Dynamical Systems Latent Dynamics Modeling
Martian · University of Oxford · University of Utah
Robustness and Leadership in Markov-switching Consensus Networks
Jun 24, 2026Sarah H. Cen, Vaibhav Srivastava, Naomi Ehrich LeonardMulti-Agent Coordination Regime-Switching Dynamical Systems
Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA · Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA · Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ 08544, USA
Deep numerical schemes for systems of Ergodic BSDEs with applications to regime-switching forward utilities
Jun 23, 2026Guillaume Broux-Quemerais, Sarah Kaakai, Anis Matoussi +1Stochastic Differential Equations Neural PDE Solvers
LMM · 1Laboratoire Manceau de Mathématiques & FR CNRS No 2962, Institut du Risque et de l’Assurance, Le Mans Université · LAGA +1
Identifiable Markov Switching Models with Instantaneous Effects and Exponential Families
Jun 1, 2026Roel Hulsman, Carles Balsells-Rodas, Sara MagliacaneTime-Series Causal Discovery Regime-Switching Dynamical Systems
University of Amsterdam · 2Adyen · 3Imperial College London +1
BAPR: Bayesian amnesic piecewise-robust reinforcement learning for non-stationary continuous control
May 15, 2026Yifan Zhang, Liang ZhengRL Control Non-Stationary RL
Central South University
Time-Varying Deep State Space Models for Sequences with Switching Dynamics
May 14, 2026Sanja Karilanova, Subhrakanti Dey, Ayça ÖzçelikkaleDynamical Systems Regime-Switching Dynamical Systems
Department of Electrical Engineering, Uppsala University, Sweden
FLUX: Geometry-Aware Longitudinal Flow Matching with Mixture of Experts
May 9, 2026Josue Ortega Caro, Yongxu Zhang, Hannah M Batchelor +3Dynamical Systems Flow Matching
Yale University
End-to-End Identifiable and Consistent Recurrent Switching Dynamical Systems
May 7, 2026Carles Balsells-Rodas, Zhengrui Xiang, Xavier Sumba +1Dynamical Systems Latent Variable Models
Imperial College London
Residual-loss Anomaly Analysis of Physics-Informed Neural Networks: An Inverse Method for Change-point Detection in Nonlinear Dynamical Systems with Regime Switching
Apr 28, 2026Yuhe Bai, Chengli Tan, Jiaqi Li +2Dynamical Systems Regime-Switching Dynamical Systems
School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, 430074, China · School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, 710072, China

Oct 7, 2026, stat.MLJ/K move · Enter open · S save

Broadly Applicable Approximate MCMC for Switching Stochastic Differential Equations Using Uniformization and Time-Conditioned Factorized Neural Likelihood Estimation

Shion Hosoda, Michiaki Hamada

Waseda University · National Institute of Advanced Industrial Science and Technology · Nippon Medical School

Stochastic Differential Equations Regime-Switching Dynamical Systems Markov Chain Monte Carlo

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Switching stochastic differential equations (SSDEs) describe continuous-time dynamics whose parameters switch according to a latent regime process that follows a continuous-time Markov chain (CTMC). By allowing dynamics to change between regimes, SSDEs represent heterogeneous system behavior and have been applied across diverse fields. However, Bayesian inference for SSDEs remains difficult, and existing SSDE inference methods have limited applicability, with restrictions such as noise-free observations, univariate states, linear drift, or state-independent diffusion. In this study, we propose an approximate Markov chain Monte Carlo sampler for SSDEs using uniformization and factorized neural likelihood estimation (FNLE), a simulation-based inference method. Uniformization provides an exact representation of the CTMC but requires SDE transition densities over arbitrary time intervals. We approximate these densities by training a time-conditioned FNLE model. The resulting sampler is broadly applicable to SSDEs without requiring analytically tractable transition densities. In synthetic-data experiments, our method recovered regime paths and parameters for three SSDE models for which previous methods have limited applicability. We also applied our method to a real dataset and detected a regime transition.

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