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Conditional and Lie symmetries of semi-linear 1D Schr\"odinger and diffusion equations are studied if the mass (or the diffusion constant) is considered as an additional variable. In this way, dynamical symmetries of semi-linear…

Mathematical Physics · Physics 2009-11-11 Stoimen Stoimenov , Malte Henkel

Transport and acceleration of charged particles in turbulent media is a topic of great interest in space physics and interstellar astrophysics. These processes are dominated by the scattering of particles off magnetic irregularities. The…

Space Physics · Physics 2016-12-28 A. Ivascenko , S. Lange , F. Spanier , R. Vainio

Generating high-quality 3D objects from textual descriptions remains a challenging problem due to computational cost, the scarcity of 3D data, and complex 3D representations. We introduce Geometry Image Diffusion (GIMDiffusion), a novel…

Computer Vision and Pattern Recognition · Computer Science 2024-09-06 Slava Elizarov , Ciara Rowles , Simon Donné

We extend a recent theory for the rheology of frictionless granular materials [K. Suzuki and H. Hayakawa, Phys. Rev. Lett. 2015, 115, 098001] to the case of frictional disks in two dimensions. Employing a frictional contact model for…

Soft Condensed Matter · Physics 2017-08-02 Kuniyasu Saitoh , Hisao Hayakawa

Probabilistic denoising diffusion models (DDMs) have set a new standard for 2D image generation. Extending DDMs for 3D content creation is an active field of research. Here, we propose TetraDiffusion, a diffusion model that operates on a…

Computer Vision and Pattern Recognition · Computer Science 2024-08-12 Nikolai Kalischek , Torben Peters , Jan D. Wegner , Konrad Schindler

In this paper, we study a Schr\"odinger-type equation featuring a derivative in the nonlinear term and incorporating diffusion effects. This type of equation arises in various physical applications, such as modeling low-order magnetization…

Analysis of PDEs · Mathematics 2025-09-30 Juan Carlos Muñoz Grajales , Deissy Marcela Pizo

We apply an analytical approach for determining the near-field radiative heat transfer between a metallic nanosphere and a planar semi-infinite medium with some given surface structure. This approach is based on a perturbative expansion,…

Mesoscale and Nanoscale Physics · Physics 2015-05-27 Svend-Age Biehs , Oliver Huth , Felix Rüting

Score-based diffusion models in infinite-dimensional function spaces provide a mathematically principled framework for modelling function-valued data, offering key advantages such as resolution invariance and the ability to handle irregular…

Machine Learning · Computer Science 2026-05-06 James Rowbottom , Elizabeth L. Baker , Nick Huang , Ben Adcock , Carola-Bibiane Schönlieb , Alexander Denker

We give the upper and the lower estimates of heat kernels for Schr\"odinger operators $H=-\Delta+V$, with nonnegative and locally bounded potentials $V$ in $\mathbb{R}^d$, $d \geq 1$. We observe a factorization: the contribution of the…

Functional Analysis · Mathematics 2023-03-13 Miłosz Baraniewicz , Kamil Kaleta

This paper exploits the theory of geometric gradient flows to introduce an alternative regularization of the thin-film equation. The solution properties of this regularization are investigated via a sequence of numerical simulations whose…

Fluid Dynamics · Physics 2020-02-20 Darryl D. Holm , Lennon Ó Náraigh , Cesare Tronci

We study Schroedinger operators with Robin boundary conditions on exterior domains in $\R^d$. We prove sharp point-wise estimates for the associated semi-groups which show, in particular, how the boundary conditions affect the time decay of…

Spectral Theory · Mathematics 2018-11-13 Hynek Kovarik , Delio Mugnolo

We discuss a probabilistic approximation framework for the three-dimensional attractive point interaction on a finite time horizon. By iterating the Doob transforms of the explicit heat kernel associated with the singular Schr\"odinger…

Probability · Mathematics 2026-05-26 Barkat Mian

This article proposes an active learning method for high dimensional data, based on intrinsic data geometries learned through diffusion processes on graphs. Diffusion distances are used to parametrize low-dimensional structures on the…

Machine Learning · Computer Science 2019-05-31 Mauro Maggioni , James M. Murphy

We consider the Dirichlet-Neumann iteration for partitioned simulation of thermal fluid-structure interaction, also called conjugate heat transfer. We analyze its convergence rate for two coupled fully discretized 1D linear heat equations…

Numerical Analysis · Mathematics 2017-05-16 Azahar Monge , Philipp Birken

Big data often has emergent structure that exists at multiple levels of abstraction, which are useful for characterizing complex interactions and dynamics of the observations. Here, we consider multiple levels of abstraction via a…

Diffusion maps are a commonly used kernel-based method for manifold learning, which can reveal intrinsic structures in data and embed them in low dimensions. However, as with most kernel methods, its implementation requires a heavy…

Machine Learning · Computer Science 2019-12-03 Scott Gigante , Jay S. Stanley , Ngan Vu , David van Dijk , Kevin Moon , Guy Wolf , Smita Krishnaswamy

We consider a simple modification of the 1D-Laplacian where non-mixed interface conditions occur at the boundaries of a finite interval. It has recently been shown that Schr\"odinger operators having this form allow a new approach to the…

Mathematical Physics · Physics 2015-06-11 Andrea Mantile

The free-particle propagator, a key operator in various algorithms for simulating the time evolution of the Schr\"odinger equation, is studied. A multiscale approximation of this propagator is constructed, representing the semigroup…

Computational Physics · Physics 2024-12-13 Evgueni Dinvay , Yuliya Zabelina , Luca Frediani

In this thesis we deal with spectral invariants for polygons and closed orbisurfaces of constant Gaussian curvature. In each case our method is to study the heat kernel and the asymptotic expansion of the heat trace. First, we investigate…

Differential Geometry · Mathematics 2017-11-10 Eren Ucar

Diffusion condensation is a dynamic process that yields a sequence of multiscale data representations that aim to encode meaningful abstractions. It has proven effective for manifold learning, denoising, clustering, and visualization of…

Machine Learning · Computer Science 2023-01-06 Guillaume Huguet , Alexander Tong , Bastian Rieck , Jessie Huang , Manik Kuchroo , Matthew Hirn , Guy Wolf , Smita Krishnaswamy
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