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Using the SEKF to Transfer NN Models of Dynamical Systems with Limited Data
🤖 AI Summary
This work addresses the high data acquisition cost often encountered in data-driven modeling of dynamical systems by proposing an efficient fine-tuning approach based on Subset Extended Kalman Filtering (SEKF). The method enables successful transfer of a pretrained neural network model to a new dynamical system using only 1% of the original training data. By integrating Bayesian estimation with dynamical system modeling, SEKF substantially reduces both data requirements and computational overhead while enhancing model generalization. Experimental validation on a damped spring-mass system and a continuous stirred-tank reactor demonstrates that fine-tuning only a small subset of parameters suffices to accurately capture the target dynamics, thereby confirming the efficacy and practicality of the proposed approach.
Technology Category
Application Category
📝 Abstract
Data-driven models of dynamical systems require extensive amounts of training data. For many practical applications, gathering sufficient data is not feasible due to cost or safety concerns. This work uses the Subset Extended Kalman Filter (SEKF) to adapt pre-trained neural network models to new, similar systems with limited data available. Experimental validation across damped spring and continuous stirred-tank reactor systems demonstrates that small parameter perturbations to the initial model capture target system dynamics while requiring as little as 1% of original training data. In addition, finetuning requires less computational cost and reduces generalization error.
Problem
Research questions and friction points this paper is trying to address.
data-driven modeling
dynamical systems
limited data
neural network adaptation
training data scarcity
Innovation
Methods, ideas, or system contributions that make the work stand out.
Subset Extended Kalman Filter
Neural Network Transfer
Data-Efficient Adaptation
Dynamical Systems Modeling
Limited Data Fine-tuning
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