Embedding stochastic differential equations into neural networks via dual processes

DOI10.1088/1742-5468/ACF126MaRDI QIDQ6607304

Naoki Sugishita, Jun Ohkubo

Publication date: 18 September 2024

Published in: Journal of Statistical Mechanics: Theory and Experiment (Search for Journal in Brave)

zbMATH Keywords

stochastic differential equationduality

Mathematics Subject Classification ID

Statistical mechanics, structure of matter (82-XX)

Cites Work

• Stochastic methods. A handbook for the natural and social sciences
• The Fokker-Planck equation. Methods of solution and applications.
• Higher-order implicit strong numerical schemes for stochastic differential equations
• Recurrent neural network-based model predictive control for continuous pharmaceutical manufacturing
• Nearest-neighbor interaction systems in the tensor-train format
• Multilayer feedforward networks are universal approximators
• Koopman operator spectrum for random dynamical systems
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Extended duality relations between birth-death processes and partial differential equations
• Numerical methods to evaluate Koopman matrix from system equations*
• Various Ways to Compute the Continuous-Discrete Extended Kalman Filter
• Ensemble Kalman filter
• Combinatorics for calculating expectation values of functions in systems with evolution governed by stochastic differential equations
• Approximation by superpositions of a sigmoidal function

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