Control Disturbance Rejection in Neural ODEs

• URL: http://arxiv.org/abs/2509.18034v1
• Date: Mon, 22 Sep 2025 17:09:17 GMT
• Title: Control Disturbance Rejection in Neural ODEs
• Authors: Erkan Bayram, Mohamed-Ali Belabbas, Tamer Başar,
• Abstract summary: We propose an iterative training algorithm for Neural ODEs that provides models resilient to control disturbances.<n>We show through simulations that this formulation enables the model to effectively learn new data points and gain against control disturbance.
• Score: 0.34410212782758043
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose an iterative training algorithm for Neural ODEs that provides models resilient to control (parameter) disturbances. The method builds on our earlier work Tuning without Forgetting-and similarly introduces training points sequentially, and updates the parameters on new data within the space of parameters that do not decrease performance on the previously learned training points-with the key difference that, inspired by the concept of flat minima, we solve a minimax problem for a non-convex non-concave functional over an infinite-dimensional control space. We develop a projected gradient descent algorithm on the space of parameters that admits the structure of an infinite-dimensional Banach subspace. We show through simulations that this formulation enables the model to effectively learn new data points and gain robustness against control disturbance.

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