yriyazi/Koopman-Operator-and-Deep-Neural-Networks-ISAV2023
In this work, we present a novel approach that combines the power of Koopman operators and deep neural networks to generate a linear representation of the Duffing oscillator. This approach enables effective parameter estimation and accurate prediction of the oscillator's future behavior.
This project helps researchers and engineers analyze and predict the behavior of complex Duffing oscillators. It takes time-series data of oscillator movement and uses a combination of advanced mathematical techniques and deep learning to estimate system parameters and forecast future states. The primary users are researchers in physics, engineering, or mathematics who study nonlinear dynamics.
Use this if you need to perform in-depth parameter estimation, long-term prediction, and qualitative analysis of Duffing oscillators using state-of-the-art methods.
Not ideal if you are dealing with a different type of nonlinear system or require a straightforward, off-the-shelf solution for general time-series forecasting without deep dynamical insights.
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Dec 01, 2025
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