A mixed-integer quadratically constrained optimization learns interpretable stable dynamical models and their Lyapunov functions from data by enforcing stability constraints during training.
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
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Learning interpretable and stable dynamical models via mixed-integer Lyapunov-constrained optimization
A mixed-integer quadratically constrained optimization learns interpretable stable dynamical models and their Lyapunov functions from data by enforcing stability constraints during training.