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math.NA2021
Active learning of tree tensor networks using optimal least-squares
Cécile Haberstich, Anthony Nouy, Guillaume Perrin
In this paper, we propose new learning algorithms for approximating high-dimensional functions using tree tensor networks in a least-squares setting. Given a dimension tree or arch…
physics.comp-ph2021★ 2 cited
On the quantification of discretization uncertainty: comparison of two paradigms
Julien Bect, Souleymane Zio, Guillaume Perrin +2
Numerical models based on partial differential equations (PDE), or integro-differential equations, are ubiquitous in engineering and science, making it possible to understand or de…