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Strengthening our grip on food security by encoding physics into AI

Marcel B.J. MeindersJack YangErik van der Linden
Nov 2023
Climate change will jeopardize food security. Food security involves the robustness of the global agri-food system. This agri-food system is intricately connected to systems centering around health, economy, social-cultural diversity, and global political stability. A systematic way to determine acceptable interventions in the global agri-food systems involves analyses at different spatial and temporal scales. Such multi-scale analyses are common within physics. Unfortunately, physics alone is not sufficient. Machine learning techniques may aid. We focus on neural networks (NN) into which physics-based information is encoded (PeNN) and apply it to a sub-problem within the agri-food system. We show that the mean squared error of the PeNN is always smaller than that of the NNs, in the order of a factor of thousand. Furthermore, the PeNNs capture extra and interpolation very well, contrary to the NNs. It is shown that PeNNs need a much smaller data set size than the NNs to achieve a similar mse. Our results suggest that the incorporation of physics into neural networks architectures yields promise for addressing food security.
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