A mixed-integer quadratically constrained optimization learns interpretable stable dynamical models and their Lyapunov functions from data by enforcing stability constraints during training.
Process structure-based recurrent neural network modeling for model predictive control of nonlinear processes
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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.