Fan Xu

Lévy noise can render the 3D torus energy-critical NLS globally well-posed, even for arbitrary initial data, by introducing a cubic lower bound on expected energy and excluding free scattering.

Robotic world models can now learn from diverse, yet confident, alternative futures, achieving state-of-the-art prediction quality and extending predictive horizons.

A unified framework for global well-posedness of the stochastic Landau--Lifshitz--Baryakhtar equation is established for initial data down to $\mathbb{L}^2$ in $\mathbb{R}^d$, $d \leq 3$.