Interpretable machine learning for shoreline forecasting

Machine learning has revolutionized scientific modeling, providing breakthroughs in fields ranging from weather prediction to protein folding. However, its adoption in physics-based domains remains limited due to the lack of interpretability in traditional black-box models. In environmental sciences, where understanding underlying mechanisms is critical, symbolic regression offers an alternative by discovering transparent mathematical expressions that can represent physical principles. In this work, we demonstrate the application of symbolic regression to physical modeling through shoreline prediction, a critical area for understanding coastal evolution under climate change and human influence. Unlike traditional physics-based models, which rely on assumptions that may not generalize across diverse coastal environments, our approach evolves a population of interpretable models directly from global observational data. By optimizing both predictive accuracy and model complexity, we uncover region-specific formulations that reveal the dominant physical drivers of shoreline change. This methodology enables data-driven discovery while maintaining alignment with physical intuition, providing new insights into physical dynamics across multiple spatial and temporal scales.