Zobrazeno 1 - 10
of 597
pro vyhledávání: '"de Hoop, Maarten"'
Autor:
Liu, Tianlin, Münchmeyer, Jannes, Laurenti, Laura, Marone, Chris, de Hoop, Maarten V., Dokmanić, Ivan
We introduce the Seismic Language Model (SeisLM), a foundational model designed to analyze seismic waveforms -- signals generated by Earth's vibrations such as the ones originating from earthquakes. SeisLM is pretrained on a large collection of open-
Externí odkaz:
http://arxiv.org/abs/2410.15765
In this paper, we study an inverse problem for an acoustic-gravitational system whose principal symbol is identical to that of an acoustic wave operator. The displacement vector of a gas or liquid between the unperturbed and perturbed flow is denoted
Externí odkaz:
http://arxiv.org/abs/2410.06360
In the theory of viscoelasticity, an important class of models admits a representation in terms of springs and dashpots. Widely used members of this class are the Maxwell model and its extended version. The paper concerns about the exact boundary con
Externí odkaz:
http://arxiv.org/abs/2408.07274
Transformers are deep architectures that define "in-context mappings" which enable predicting new tokens based on a given set of tokens (such as a prompt in NLP applications or a set of patches for a vision transformer). In this work, we study in par
Externí odkaz:
http://arxiv.org/abs/2408.01367
This paper is motivated by recent works on inverse problems for acoustic wave propagation in the interior of gas giant planets. In such planets, the speed of sound is isotropic and tends to zero at the surface. Geometrically, this corresponds to a Ri
Externí odkaz:
http://arxiv.org/abs/2406.19734
We provide a new method for constructing the anisotropic relaxation tensor and proving its exponential decay property for the extended Burgers model (abbreviated by EBM). The EBM is an important viscoelasticity model in rheology, and used in Earth an
Externí odkaz:
http://arxiv.org/abs/2406.18978
This work introduces a sampling method capable of solving Bayesian inverse problems in function space. It does not assume the log-concavity of the likelihood, meaning that it is compatible with nonlinear inverse problems. The method leverages the rec
Externí odkaz:
http://arxiv.org/abs/2405.15676
Reducing the cost of posterior sampling in linear inverse problems via task-dependent score learning
Score-based diffusion models (SDMs) offer a flexible approach to sample from the posterior distribution in a variety of Bayesian inverse problems. In the literature, the prior score is utilized to sample from the posterior by different methods that r
Externí odkaz:
http://arxiv.org/abs/2405.15643
Autor:
Kratsios, Anastasis, Furuya, Takashi, Benitez, Jose Antonio Lara, Lassas, Matti, de Hoop, Maarten
In this paper, we construct a mixture of neural operators (MoNOs) between function spaces whose complexity is distributed over a network of expert neural operators (NOs), with each NO satisfying parameter scaling restrictions. Our main result is a \t
Externí odkaz:
http://arxiv.org/abs/2404.09101
On gas giant planets the speed of sound is isotropic and goes to zero at the surface. Geometrically, this corresponds to a Riemannian manifold whose metric tensor has a conformal blow-up near the boundary. The blow-up is tamer than in asymptotically
Externí odkaz:
http://arxiv.org/abs/2403.05475