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arXiv Machine Learning By Divya Sanghi, Carlos E. S. Cesnik

A Residual Learning Approach for Unsteady Aerodynamic Load Prediction

Read the original on arXiv Machine Learning →

The paper explores using residual learning with an LSTM neural network to enhance unsteady aerodynamic load predictions for aeroelastic applications. By training the network on the difference between high‑fidelity CFD lift data and a physics‑based Wagner model for the NLR 7301 airfoil in transonic flow, the residual approach consistently outperforms a direct neural‑network model in most tests, especially in generalization scenarios. The study demonstrates that residual learning can effectively augment classical low‑order aerodynamic theories by learning a lower‑variance correction to the baseline physics model.

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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