Helix-FNO: Spectral-Domain Operator Learning Coupled with a High-Fidelity Mechanistic Model for Fast Surrogate Simulation
Read the original on arXiv Machine Learning →Helix‑FNO is a teacher‑student framework that couples a 32‑state mechanistic model with a Fourier neural operator to learn the full solution operator for full‑scale treatment processes. The teacher generates a high‑fidelity dataset via Latin‑hypercube sampling and active learning, while the student learns in the spectral domain, enabling generalisation across varying influent profiles, controls, and plant layouts. The resulting operator achieves millisecond inference, three orders of magnitude faster than the mechanistic teacher, and is evaluated against physics‑informed and data‑driven surrogates on accuracy, dataset efficiency, and latency, positioning it on a speed‑accuracy Pareto front.
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