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Accurate and efficient predictions of the quasiparticle properties of complex materials remain a major challenge due to the convergence issue and the unfavorable scaling of the computational cost with respect to the system size.…

Materials Science · Physics 2020-07-14 Weiyi Xia , Weiwei Gao , Gabriel Lopez-Candales , Yabei Wu , Wei Ren , Wenqing Zhang , Peihong Zhang

We propose a data processing technique that allows searches for a stochastic background of gravitational radiation with data from a single detector. Our technique exploits the difference between the coherence time of the gravitational wave…

General Relativity and Quantum Cosmology · Physics 2012-05-22 Massimo Tinto , J. W. Armstrong

Variable-order time-fractional wave equations provide a flexible model for wave phenomena with evolving memory effects and anomalous temporal dynamics. Their numerical approximation is challenging because the variable-order fractional…

Numerical Analysis · Mathematics 2026-05-26 Lu Zhang

While geostrophy remains the simplest and most practical balance to extract velocity information from sea-surface height anomaly (SSHa), confusions remain within the oceanographic community to what extent this balance can be applied to…

Atmospheric and Oceanic Physics · Physics 2026-01-27 Takaya Uchida , Badarvada Yadidya , Vadim Bertrand , Jia-Xian Chang , Brian Arbic , Jay Shriver , Julien Le Sommer

The free expansion of a Gaussian wavepacket is a problem commonly discussed in undergraduate quantum classes by directly solving the time-dependent Schrodinger equation as a differential equation. In this work, we provide an alternative way…

Quantum Physics · Physics 2023-06-07 Alessandro M. Orjuela , J. K. Freericks

We introduce a new scalable variational Gaussian process approximation which provides a high fidelity approximation while retaining general applicability. We propose the harmonic kernel decomposition (HKD), which uses Fourier series to…

Machine Learning · Computer Science 2021-06-14 Shengyang Sun , Jiaxin Shi , Andrew Gordon Wilson , Roger Grosse

3D Gaussian splatting (3DGS) has recently emerged as an alternative representation that leverages a 3D Gaussian-based representation and introduces an approximated volumetric rendering, achieving very fast rendering speed and promising…

Computer Vision and Pattern Recognition · Computer Science 2024-08-08 Joo Chan Lee , Daniel Rho , Xiangyu Sun , Jong Hwan Ko , Eunbyung Park

Recent advances in Bayesian learning with large-scale data have witnessed emergence of stochastic gradient MCMC algorithms (SG-MCMC), such as stochastic gradient Langevin dynamics (SGLD), stochastic gradient Hamiltonian MCMC (SGHMC), and…

Machine Learning · Statistics 2016-10-24 Changyou Chen , Nan Ding , Lawrence Carin

Numerical simulations of merging black hole binaries produce the most accurate gravitational waveforms. The availability of hundreds of these numerical relativity (NR) waveforms, often containing many higher spherical harmonic modes, allows…

General Relativity and Quantum Cosmology · Physics 2016-11-24 Chad R. Galley , Patricia Schmidt

We examine an extension to the theory of Gaussian wave packet dynamics in a one-dimensional potential by means of a sequence of time dependent displacement and squeezing transformations. Exact expressions for the quantum dynamics are found,…

Atomic Physics · Physics 2009-10-31 B. M. Garraway

We derive a geometric integration formula for the partition function of a classical dynamical system and use it to show that corrections to the WKB approximation vanish for any Hamiltonian which generates conformal motions of some…

High Energy Physics - Theory · Physics 2009-10-28 L. D. Paniak , G. W. Semenoff , R. J. Szabo

We consider the geometric ergodicity of the Stochastic Gradient Langevin Dynamics (SGLD) algorithm under nonconvexity settings. Via the technique of reflection coupling, we prove the Wasserstein contraction of SGLD when the target…

Probability · Mathematics 2024-08-27 Lei Li , Jian-Guo Liu , Yuliang Wang

In this work we study the non-parametric reconstruction of spatio-temporal dynamical Gaussian processes (GPs) via GP regression from sparse and noisy data. GPs have been mainly applied to spatial regression where they represent one of the…

Machine Learning · Computer Science 2020-10-06 Marco Todescato , Andrea Carron , Ruggero Carli , Gianluigi Pillonetto , Luca Schenato

An increasing number of machine learning tasks deal with learning representations from set-structured data. Solutions to these problems involve the composition of permutation-equivariant modules (e.g., self-attention, or individual…

Machine Learning · Computer Science 2021-03-09 Navid Naderializadeh , Soheil Kolouri , Joseph F. Comer , Reed W. Andrews , Heiko Hoffmann

Gaussian beams are asymptotically valid high frequency solutions to hyperbolic partial differential equations, concentrated on a single curve through the physical domain. They can also be extended to some dispersive wave equations, such as…

Numerical Analysis · Mathematics 2011-06-03 Hailiang Liu , Olof Runborg , Nicolay M. Tanushev

3D Gaussian splatting (3DGS) has demonstrated exceptional performance in image-based 3D reconstruction and real-time rendering. However, regions with complex textures require numerous Gaussians to capture significant color variations…

Computer Vision and Pattern Recognition · Computer Science 2025-06-10 Binxiao Huang , Zhihao Li , Shiyong Liu , Xiao Tang , Jiajun Tang , Jiaqi Lin , Yuxin Cheng , Zhenyu Chen , Xiaofei Wu , Ngai Wong

We propose a meshless conservative Galerkin method for solving Hamiltonian wave equations. We first discretize the equation in space using radial basis functions in a Galerkin-type formulation. Differ from the traditional RBF Galerkin…

Numerical Analysis · Mathematics 2022-09-20 Zhengjie Sun , Leevan Ling

Nearly all template-based gravitational wave (GW) searches only include the quasi-circular quadrupolar modes of the signals in their templates. Including additional degrees of freedom in the GW templates corresponding to higher-order…

General Relativity and Quantum Cosmology · Physics 2024-09-10 Digvijay Wadekar , Tejaswi Venumadhav , Javier Roulet , Ajit Kumar Mehta , Barak Zackay , Jonathan Mushkin , Matias Zaldarriaga

In this paper, we present a new variational integrator for problems in Lagrangian mechanics. Using techniques from Galerkin variational integrators, we construct a scheme for numerical integration that converges geometrically, and is…

Numerical Analysis · Mathematics 2012-11-20 James Hall , Melvin Leok

Due to the effectiveness of second-order algorithms in solving classical optimization problems, designing second-order optimizers to train deep neural networks (DNNs) has attracted much research interest in recent years. However, because of…

Machine Learning · Computer Science 2024-03-06 Ying Sun , Hongwei Yong , Lei Zhang
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