Generalizable physics-constrained modeling using learning and inference assisted by feature-space engineering
نویسندگان
چکیده
We present a data-driven framework that uses very limited data to learn generalizable bypass transition models. Trained on just two flat plate cases, the augmented model is shown generalize other cases and turbomachinery configurations.
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ژورنال
عنوان ژورنال: Physical review fluids
سال: 2021
ISSN: ['2469-9918', '2469-990X']
DOI: https://doi.org/10.1103/physrevfluids.6.124602