Stochastic Differential Equations

Also known as SDE

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21 papers in the last four weeks, up 320% on the four weeks before. 0.1% of all new papers.

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Deep Learning and Elicitability for McKean-Vlasov FBSDEs With Common Noise
Dec 16, 2025Felipe J. P. Antunes, Yuri F. Saporito, Sebastian JaimungalStochastic Differential Equations Mean Field Games
School of Applied Mathematics, Getulio Vargas Foundation, Brazil. · Oxford-Man Institute for Quantitative Finance, University of Oxford
Neural Stochastic Differential Equations on Compact State Spaces: Theory, Methods, and Application to Suicide Risk Modeling
Aug 23, 2025Malinda Lu, Yue-Jane Liu, Matthew K. Nock +1Stochastic Differential Equations Latent Dynamics Modeling
Wellesley College · Harvard University
Sig-DEG for Distillation: Making Diffusion Models Faster and Lighter
Aug 23, 2025Wen Ge, Lei Jiang, Lingyi Yang +7Diffusion Model Distillation Diffusion Model Inference Acceleration
University College London · University of Oxford · JPMorganChase +1
The Effect of Stochasticity in Score-Based Diffusion Sampling: a KL Divergence Analysis
Jun 13, 2025Bernardo P. Schaeffer, Ricardo M. S. Rosa, Glauco ValleStochastic Differential Equations Diffusion Sampling
Instituto de Matem´atica, Universidade Federal do Rio de Janeiro
A Jump-Diffusion Framework for Irregular Time Series Generation
May 29, 2025O. Pfohl, J. Chemseddine, P. Hagemann +3Irregular Time-Series Modeling Stochastic Differential Equations
Department of Mathematics, Humboldt-Universität zu Berlin, 12489, Berlin, Germany · Department of Mathematics, Technische Universität Berlin, 10623, Berlin, Germany · Bundesanstalt für Materialforschung und -prüfung, 12205, Berlin, Germany +1
Rough Stochastic Pontryagin Maximum Principle and an Indirect Shooting Method
Feb 10, 2025Thomas LewStochastic Differential Equations Stochastic Optimal Control
Toyota Research Institute
Deep Operator BSDE: a Numerical Scheme to Approximate Solution Operators
Dec 4, 2024Pere Diaz-Lozano, Giulia Di NunnoStochastic Differential Equations
Department of Mathematics, University of Oslo, Oslo, Norway
Polynomial Scaling is Possible For Neural Operator Approximations of Structured Families of BSDEs
Oct 18, 2024Takashi Furuya, Anastasis KratsiosStochastic Differential Equations PDE Solving
Doshisha University, Japan · Department of Mathematics McMaster University and Vector Institute, Canada
DeepSPoC: A Deep Learning Based Sequential Propagation of Chaos
Aug 29, 2024Kai Du, Yongle Xie, Tao Zhou +1Stochastic Differential Equations Neural PDE Solvers
Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, 200438, China. · Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, 200433, China. · LSEC, Institute of Computational Mathematics and Scientific/Engineering Computing, AMSS, Chinese Academy of Sciences, Beijing, China.
Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data
Feb 22, 2024YongKyung Oh, Dong-Young Lim, Sungil KimIrregular Time-Series Modeling Time Series Imputation
Ulsan National Institute of Science and Technology, Republic of Korea
Deep learning based numerical approximation algorithms for stochastic partial differential equations
Dec 2, 2020Christian Beck, Sebastian Becker, Patrick Cheridito +2Stochastic Differential Equations Neural PDE Solvers
Department of Mathematics, ETH Zurich · School of Data Science and School of Artificial Intelligence,2025 The Chinese University of Hong Kong, Shenzhen (CUHK-Shenhen) · Applied Mathematics: Institute for Analysis and Numerics, University of Münster +1

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Deep Learning and Elicitability for McKean-Vlasov FBSDEs With Common Noise

Felipe J. P. Antunes, Yuri F. Saporito, Sebastian Jaimungal

School of Applied Mathematics, Getulio Vargas Foundation, Brazil. · Oxford-Man Institute for Quantitative Finance, University of Oxford

Stochastic Differential Equations Mean Field Games

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We present a novel numerical method for solving McKean-Vlasov forward-backward stochastic differential equations (MV--FBSDEs) with common noise, combining Picard iterations, elicitability and deep learning. The key innovation involves elicitability to derive a path-wise loss function, enabling efficient training of neural networks to approximate both the backward process and the conditional expectations arising from common noise -- without requiring computationally expensive nested Monte Carlo simulations. The mean-field interaction term is parameterized via a recurrent neural network trained to minimize an elicitable score, while the backward process is approximated through a hybrid feedforward and recurrent network representing the decoupling field. We validate the algorithm on a systemic risk inter-bank borrowing and lending model, where analytical solutions exist, demonstrating accurate recovery of the true solution. We further extend the model to quantile--mediated interactions, showcasing the flexibility of the elicitability framework beyond conditional means or moments. Finally, we apply the method to a non-stationary Aiyagari-Bewley-Huggett economic growth model with endogenous interest rates, illustrating its applicability to complex mean-field games without closed-form solutions.