11 citations · 19 across the 12 of their papers we have counts for
4 papers · 1 filter
Rates and architectures for learning geometrically non-trivial operators
T. Mitchell Roddenberry, Leo Tzou, Ivan Dokmanić +2
Deep learning methods have proven capable of recovering operators between high-dimensional spaces, such as solution maps of PDEs and similar objects in mathematical physics, from v…
Preconditioned Langevin Dynamics with Score-Based Generative Models for Infinite-Dimensional Linear Bayesian Inverse Problems
Lorenzo Baldassari, Josselin Garnier, Knut Solna +1
Designing algorithms for solving high-dimensional Bayesian inverse problems directly in infinite-dimensional function spaces - where such problems are naturally formulated - is cru…
Principal spectral rigidity implies subprincipal spectral rigidity
Maarten V. de Hoop, Joonas Ilmavirta, Vitaly Katsnelson
We study the inverse spectral problem of jointly recovering a radially symmetric Riemannian metric and an additional coefficient from the Dirichlet spectrum of a perturbed Laplace-…
Neural equilibria for long-term prediction of nonlinear conservation laws
J. Antonio Lara Benitez, Kareem Hegazy, Junyi Guo +3
Nonlinear conservation laws govern a broad class of important physical systems in science and industry and are central to scientific machine learning (SciML). Large general-purpose…