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Symbolic Regression in Scientific Machine Learning: From Data Noise to Governing Equations

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Introduction In recent years, machine learning has become widely used in scientific contexts, producing strong predictive results. However, an increasingly clear limitation has emerged: many models perform well numerically but remain difficult to interpret. At ActarusLab, our work focuses on this gap. We explore whether it is possible, starting https://baltasarq900uql5.blogoxo.com/profile

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