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Discovering conservation laws using optimal transport and manifold learning - Nature Communications

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Discovering conservation laws using optimal transport and manifold learning - Nature Communications

Conservation laws are key theoretical and practical tools for understanding, characterizing, and modeling nonlinear dynamical systems. However, for many complex systems, the corresponding conserved quantities are difficult to identify, making it hard to analyze their dynamics and build stable predictive models. Current approaches for discovering conservation laws often depend on detailed dynamical information or rely on black box parametric deep learning methods.

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Discovering conservation laws using optimal transport and manifold learning - Nature Communications

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Discovering conservation laws using optimal transport and manifold learning - Nature Communications

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