Nicolas Boullé

Learning coordinates while enforcing algebraic constraints leads to more compact and coherent representations of complex dynamical systems than traditional methods.

Data-driven Koopman operator approximations can now be validated and improved using a novel principal angle decomposition, leading to more reliable spectral analysis and forecasting.