1 citations · 1 across the 3 of their papers we have counts for
8 papers
Propagation of singularities and inverse problems for the viscoacoustic wave equation
Giovanni Covi, Maarten de Hoop, Mikko Salo
We study an inverse problem for the viscoacoustic wave equation, an integro-differential model describing wave propagation in viscoacoustic media with memory in the leading order t…
Neural Operators Can Discover Functional Clusters
Yicen Li, Jose Antonio Lara Benitez, Ruiyang Hong +3
Operator learning is reshaping scientific computing by amortizing inference across infinite families of problems. While neural operators (NOs) are increasingly well understood for…
Hybrid operator learning of wave scattering maps in high-contrast media
Advait Balaji, Trevor Teolis, S. David Mis +3
Surrogate modeling of wave propagation and scattering (i.e. the wave speed and source to wave field map) in heterogeneous media has significant potential in applications such as se…
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-…