A new local time-decoupled squared Wasserstein-2 method for training stochastic neural networks to reconstruct uncertain parameters in dynamical systems

• URL: http://arxiv.org/abs/2503.05068v1
• Date: Fri, 07 Mar 2025 01:20:43 GMT
• Title: A new local time-decoupled squared Wasserstein-2 method for training stochastic neural networks to reconstruct uncertain parameters in dynamical systems
• Authors: Mingtao Xia, Qijing Shen, Philip Maini, Eamonn Gaffney, Alex Mogilner,
• Abstract summary: We show that a network neural model can be effectively trained by minimizing our proposed local time-decoupled squared Wasserstein-2 loss function.<n>We showcase the effectiveness of our proposed method in reconstructing the distribution of parameters in different dynamical systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we propose and analyze a new local time-decoupled squared Wasserstein-2 method for reconstructing the distribution of unknown parameters in dynamical systems. Specifically, we show that a stochastic neural network model, which can be effectively trained by minimizing our proposed local time-decoupled squared Wasserstein-2 loss function, is an effective model for approximating the distribution of uncertain model parameters in dynamical systems. Through several numerical examples, we showcase the effectiveness of our proposed method in reconstructing the distribution of parameters in different dynamical systems.

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