Embedding stochastic differential equations into neural networks via dual processes

• URL: http://arxiv.org/abs/2306.04847v2
• Date: Thu, 17 Aug 2023 12:29:49 GMT
• Title: Embedding stochastic differential equations into neural networks via dual processes
• Authors: Naoki Sugishita and Jun Ohkubo
• Abstract summary: We propose a new approach to constructing a neural network for predicting expectations of differential equations. The proposed method does not need data sets of inputs and outputs. As a demonstration, we construct neural networks for the Ornstein-Uhlenbeck process and the noisy van der Pol system.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a new approach to constructing a neural network for predicting expectations of stochastic differential equations. The proposed method does not need data sets of inputs and outputs; instead, the information obtained from the time-evolution equations, i.e., the corresponding dual process, is directly compared with the weights in the neural network. As a demonstration, we construct neural networks for the Ornstein-Uhlenbeck process and the noisy van der Pol system. The remarkable feature of learned networks with the proposed method is the accuracy of inputs near the origin. Hence, it would be possible to avoid the overfitting problem because the learned network does not depend on training data sets.

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