Stochastic Differential Equations

Also known as SDE

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17 papers in the last four weeks, up 467% on the four weeks before. 0.1% 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
Boundary-aware Reinforcement Learning for Hypercube State Spaces via Deterministic Policy Gradient
Oct 7, 2026Lijun Bo, Yijie Huang, Chenhao LuRL Control Reinforcement Learning
Learning Transition Kernels of Jump-Diffusion Processes with Conditional Diffusion Models
Oct 6, 2026Yuzhen Zhao, Yating Liu, Quentin GuibertStochastic Differential Equations Conditional Distribution Estimation
CEREMADE, CNRS, Universite Paris-Dauphine, PSL, 75016 Paris, France
HyperNSDE: Personalized Neural SDEs for Joint Static-Longitudinal Clinical Data Generation
Oct 5, 2026Perrine Chassat, Agathe GuillouxStochastic Differential Equations Synthetic Data Generation
Inria, Université Paris Cité, Inserm, HeKA F-75015 Paris, France
Sharp dimensional analysis of midpoint methods for Langevin sampling
Oct 5, 2026Fan Chen, Sinho Chewi, Jianfeng Lu +1Markov Chain Monte Carlo Stochastic Differential Equations
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. · Department of Statistics and Data Science, Yale University. · Department of Mathematics, Duke University. +1
Langevin Flow Maps: Efficient Molecular Dynamics and Transition Path Sampling
Oct 5, 2026Sam McCallum, Niklas Rindtorff, Alexander Tong +1Stochastic Differential Equations Machine Learning Interatomic Potentials
University of Bath · AITHYRA
Gaussian Flow Dynamics: Simulation-Free Neural SDE Learning Beyond One-Time Marginals
Oct 3, 2026Grigory Bartosh, Christian A. NaessethStochastic Differential Equations Latent Dynamics Modeling
Informatics Institute University of Amsterdam
Probability flow ODEs in score-based and reflected diffusion models
Oct 2, 2026Rama CONTDiffusion Sampling Diffusion Models
Mathematical Institute, University of Oxford
SDECast: Probabilistic Weather Forecasting in Continuous Time with Neural SDEs
Oct 2, 2026Maria Marchenko, Martin Andrae, Fredrik Lindsten +1Weather Forecasting Probabilistic Forecasting
Linköping University, Sweden
Adaptive Second-Order Solvers for Fast Stochastic Diffusion Sampling
Oct 2, 2026Ella Kemperman, Luca AmbrogioniDiffusion Sampling Diffusion Timestep Scheduling
Radboud University
Tangent Schrödinger Bridge Matching: Learning Stochastic Transport with Mechanistic Sensitivities
Oct 2, 2026Jowaria Khan, Elizabeth Bondi-KellySchrödinger Bridges Optimal Transport
University of Michigan
Inference for stochastic differential equations driven by weighted sub-fractional Brownian motion using neural networks and the Euler approximation
Sep 30, 2026J. H. Ramirez-GonzalezParameter Estimation Stochastic Differential Equations
Institute of Mathematics and Statistics, University of São Paulo (IME–USP), São Paulo, Brazil
Learning Continuous Neural Representation of Stochastic Hybrid Systems
Sep 30, 2026Sangli Teng, Hang Liu, Koushil SreenathDynamical Systems Stochastic Differential Equations
Unified Optimality Conditions for Stochastic Optimal Control in the Rough Path and Itô Frameworks
Sep 29, 2026Thomas LewStochastic Differential Equations Stochastic Optimal Control
Toyota Research Institute
Identifiability Guarantees for Drivers and Dynamics of Delayed Physical Systems
Sep 29, 2026Julien Boussard, Antoine Debouchage, Théo SaulusStochastic Differential Equations Causal Identifiability
School of Computer Science, McGill University, Canada · Mila - Quebec AI Institute, Canada · LaMMe, Université Évry Paris-Saclay, France +1
Structured Neural SDEs for Functional Calibration
Sep 28, 2026Francesco Piatti, Andrea Iannucci, Thomas CassStochastic Differential Equations
Department of Mathematics, Imperial College London
Simulation-Free Learning of GP-SDEs from Irregular Observations
Sep 27, 2026Zhidi Lin, Yuhao Liu, Ying Li +2Stochastic Differential Equations State Space Models
The University of Hong Kong · Capital One · National University of Singapore +1
Continuous-Time Trajectory Generation from Discrete Observations with Stochasticity
Sep 25, 2026Ruifeng Shang, Shu Liu, Yuhua ZhuTrajectory Generation Flow-Based Generative Modeling
Department of Mathematics Florida State University Tallahassee, FL 32306, USA · Department of Statistics and Data Science University of California, Los Angeles Los Angeles, CA 90095, USA
First-Order Stationarity of Reverse Diffusions
Sep 25, 2026Zhifeng Chen, Chenyang Jiang, Yazhen WangDiffusion Models Diffusion Sampling
Department of Statistics, UW-Madison
One Intervention per Component is Enough: Towards Identifiability in Linear Stochastic Dynamics from Steady State
Sep 17, 2026Saber SalehkaleybarParameter Estimation Stochastic Differential Equations
Leiden Institute of Advanced Computer Science, Leiden University, The Netherlands
Backward SDEs-based Diffusion for Physics-Constrained Generation
Sep 14, 2026Zihao WangDiffusion Model Inversion Constrained Generative Modeling
A Splitting Method for SDE Terminal-Law Estimation
Sep 11, 2026Rushil Gupta, Sandeep JunejaStochastic Differential Equations Diffusion Model Sampling
Safexpress Centre for Data, Learning and Decision Sciences Ashoka University
Deep operator learning for efficient sampling from invariant measures of stochastic differential equations
Sep 10, 2026Ling Guo, Lei Li, Jingtong ZhangStochastic Differential Equations Diffusion Sampling
Department of Mathematics, Shanghai Normal University, Shanghai, China · School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China · Institute of Natural Sciences, MOE-LSC, Shanghai Jiao Tong University, Shanghai, China
Quantitative Target Convergence and Uniform-in-Time Propagation of Chaos for Langevin-Regularized SVGD
Aug 28, 2026Sayan Banerjee, Dohyeon KimStein Variational Gradient Descent Stochastic Differential Equations
Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, 318 Hanes Hall, CB# 3260, Chapel Hill, NC 27599-3260, USA. · Department of Computing and Mathematical Sciences, California Institute of Technology, 1200 East California Boulevard, MC 305-16, Pasadena, CA 91125, USA.
Stochastic Liquid Deformation Fields: An SDE Generalisation of Closed-Form Continuous-Time Cells for Dynamic 3D Gaussian Splatting
Aug 27, 2026Mingzhao Li, Arghya Pal3D Gaussian Splatting Dynamic Scene Reconstruction
School of Information Technology, Monash University, Selangor, Malaysia
Deep-MKV-TS: Path-Dependent McKean--Vlasov Control for Financial Time Series Generation
Aug 19, 2026Samer El Boustany, Théo Basseras, Samy Mekkaoui +3Financial Time Series Stochastic Optimal Control
Murex SAS · École Polytechnique · BNP-PAR and Ecole Polytechnique
A Hyperfinite Framework for Score-Based Generative Modeling
Aug 3, 2026Sunder Ram KrishnanScore Matching Stochastic Differential Equations
Kerala School of Mathematics, Kozhikode 673571, Kerala, India.
LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems
Aug 3, 2026Shida Liu, Abhishek Gupta, Sumit Sinha +1Stochastic Differential Equations Lie Group Methods
School of Engineering and Applied Sciences (SEAS), Harvard University, Cambridge, MA, USA. · Tracelink, Wilmington, MA, USA. · SEAS, Physics and OEB, Harvard University, Cambridge, MA, USA.
Reflected diffusion, no-flux continuity equations and confined Lagrangian flows in bounded domains
Jul 30, 2026Rama ContDynamical Systems Stochastic Differential Equations
Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise
Jul 21, 2026Arthur Bizzi, Olga FinkDynamical Systems Stochastic Differential Equations
EPFL, Imperial College · EPFL

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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.