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Internal representations are crucial for understanding deep neural networks, such as their properties and reasoning patterns, but remain difficult to interpret. While mapping from feature space to input space aids in interpreting the…

Computer Vision and Pattern Recognition · Computer Science 2026-01-15 Nils Neukirch , Johanna Vielhaben , Nils Strodthoff

This work is concerned with a representation of shapes that disentangles fine, local and possibly repeating geometry, from global, coarse structures. Achieving such disentanglement leads to two unrelated advantages: i) a significant…

Computer Vision and Pattern Recognition · Computer Science 2022-04-06 Luca Morreale , Noam Aigerman , Paul Guerrero , Vladimir G. Kim , Niloy J. Mitra

This paper studies the problem of distributed formation maneuver control of multi-agent systems via complex Laplacian. We will show how to change the translation, scaling, rotation, and also the shape of formation continuously by only…

Systems and Control · Electrical Eng. & Systems 2023-12-08 Xu Fang , Lihua Xie

The inversion theorem and convolution theorem of the conformable fractional Laplace transforms are developed. All the elementary properties of the classical Laplace transform are extended to the conformable fractional transform, and using…

Dynamical Systems · Mathematics 2026-05-13 Somnath Sarate , Anil Khairnar , Krishnat Masalkar

We consider acoustic waves propagating in an inviscid fluid interacting with a rigid periodically perforated plate in the presence of permanent flows. The paper presents a model of an acoustic interface obtained by the asymptotic…

Analysis of PDEs · Mathematics 2023-09-11 Eduard Rohan , Vladimír Lukeš

Learning a graph with a specific structure is essential for interpretability and identification of the relationships among data. It is well known that structured graph learning from observed samples is an NP-hard combinatorial problem. In…

Machine Learning · Statistics 2019-09-26 Sandeep Kumar , Jiaxi Ying , Jos'e Vin'icius de M. Cardoso , Daniel P. Palomar

The interaction between boundary layer turbulence and a porous layer is the cornerstone to the interface engineering. In this study, the spatial resolved transfer entropy is used to assess the asymmetry of the causal interaction next to a…

Fluid Dynamics · Physics 2023-12-04 Xu Chu , Wenkang Wang , Bernhard Weigand

The Material Point Method (MPM) is a hybrid Eulerian-Lagrangian approach capable of simulating large deformation problems of history-dependent materials. While the MPM can represent complex and evolving material domains by using Lagrangian…

Geophysics · Physics 2019-10-01 Ezra Y. S. Tjung , Shyamini Kularathna , Krishna Kumar , Kenichi Soga

In this paper we consider the computational complexity of uniformizing a domain with a given computable boundary. We give nontrivial upper and lower bounds in two settings: when the approximation of boundary is given either as a list of…

Complex Variables · Mathematics 2007-05-23 Ilia Binder , Mark Braverman , Michael Yampolsky

In this paper, we present a stochastic method for the simulation of Laplace's equation with a mixed boundary condition in planar domains that are polygonal or bounded by circular arcs. We call this method the Reflected Walk-on-Spheres…

Numerical Analysis · Mathematics 2024-07-10 Qiansheng Han , Antti Rasila , Tommi Sottinen

Spectral geometric methods have brought revolutionary changes to the field of geometry processing. Of particular interest is the study of the Laplacian spectrum as a compact, isometry and permutation-invariant representation of a shape.…

We consider the radiation transfer problem in the discrete-ordinate, plane-parallel approach. We introduce two benchmark problems with exact known solutions and show that for strongly non-homogeneous media the homogeneous layers…

Computational Physics · Physics 2007-05-23 M. -P. Zorzano , A. M. Mancho , L. Vazquez

In this work we use reiterated homogenization and unfolding operator approach to study the asymptotic behavior of the solutions of the $p$-Laplacian equation with Neumann boundary conditions set in a rough thin domain with concentrated…

Analysis of PDEs · Mathematics 2020-11-02 Ariadne Nogueira , Jean Carlos Nakasato

Learning maps between data samples is fundamental. Applications range from representation learning, image translation and generative modeling, to the estimation of spatial deformations. Such maps relate feature vectors, or map between…

Computer Vision and Pattern Recognition · Computer Science 2021-06-18 Hastings Greer , Roland Kwitt , Francois-Xavier Vialard , Marc Niethammer

Recent work of the authors and their collaborators has uncovered fundamental connections between the Dirichlet-to-Neumann map, the spectral flow of a certain family of self-adjoint operators, and the nodal deficiency of a Laplacian…

Spectral Theory · Mathematics 2023-03-07 Gregory Berkolaiko , Graham Cox , Bernard Helffer , Mikael Persson Sundqvist

We show that the computational complexity of Riemann mappings can be bounded by the complexity needed to compute conformal mappings locally at boundary points. As a consequence we get first formally proven upper bounds for…

Computational Complexity · Computer Science 2010-06-03 Robert Rettinger

In the present work, we study and analyze an efficient iterative coupling method for a dimensionally heterogeneous problem . We consider the case of 2-D Laplace equation with non symmetric boundary conditions with a corresponding 1-D…

Analysis of PDEs · Mathematics 2012-12-19 Manel Tayachi Pigeonnat , Antoine Rousseau , Eric Blayo , Nicole Goutal , Véronique Martin

We present a numerical method for the computation of the conformal map from unbounded multiply-connected domains onto lemniscatic domains. For $\ell$-times connected domains the method requires solving $\ell$ boundary integral equations…

Complex Variables · Mathematics 2019-08-26 Mohamed M. S. Nasser , Jörg Liesen , Olivier Sète

We discuss the limit of small width for the Laplacian defined on a waveguide with Robin boundary conditions. Under suitable hypothesis on the scaling of the curvature, we prove the convergence of the Robin Laplacian to the Laplacian on the…

Mathematical Physics · Physics 2008-06-04 C. Cacciapuoti , D. Finco

We introduce Hodge Diffusion Maps, a novel manifold learning algorithm designed to analyze and extract topological information from high-dimensional data-sets. This method approximates the exterior derivative acting on differential forms,…

Machine Learning · Computer Science 2025-04-11 Alvaro Almeida Gomez , Jorge Duque Franco
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