A constrained symbolic regression method on expression trees discovers Lyapunov functions for autonomous dynamical systems without assuming their functional form.
Globally Optimal Symbolic Regression
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abstract
In this study we introduce a new technique for symbolic regression that guarantees global optimality. This is achieved by formulating a mixed integer non-linear program (MINLP) whose solution is a symbolic mathematical expression of minimum complexity that explains the observations. We demonstrate our approach by rediscovering Kepler's law on planetary motion using exoplanet data and Galileo's pendulum periodicity equation using experimental data.
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eess.SY 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A symbolic decision tree method is introduced to simultaneously learn interpretable regime partitions and local governing equations using basis function parametrization and mixed-integer optimization.
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A constrained symbolic regression approach for Lyapunov function discovery
A constrained symbolic regression method on expression trees discovers Lyapunov functions for autonomous dynamical systems without assuming their functional form.
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Learning regime-dependent governing equations: A symbolic decision tree approach
A symbolic decision tree method is introduced to simultaneously learn interpretable regime partitions and local governing equations using basis function parametrization and mixed-integer optimization.