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arXiv Machine Learning By Masahisa Watanabe, Shiva Dixit, Nirmal Punetha, Swati Chauhan, Manish Dev Shirimali

Prediction of Nonlinear Oscillations in a Jumping Quarter-Car Model Using Reservoir Computing

Read the original on arXiv Machine Learning →

The paper investigates using reservoir computing, specifically an echo state network, to predict nonlinear dynamics in a jumping quarter‑car model. By training on limited time‑series data, the network can reconstruct bifurcation diagrams, phase‑space attractors, and time trajectories across both periodic and chaotic regimes, reproducing the period‑doubling route to chaos. These findings show that reservoir computing can serve as a data‑driven predictor for nonlinear, non‑smooth vehicle dynamics.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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