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Related papers: Tensor network methods for the Gross-Pitaevskii eq…

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Optimal exploitation of supercomputing resources for the evaluation of electrostatic forces remains a challenge in molecular dynamics simulations of very large systems. The most efficient methods are currently based on particle-mesh Ewald…

Computational Physics · Physics 2025-09-23 Federica Troni , Davide Grassano , Jayashree Narayan , Benoît Roux , Sara Bonella

We present a tensor network model (TNM) for forecasting nonlinear and chaotic dynamics, bridging quantum many-body methods with classical complex systems. The TNM leverages hierarchical tensor contractions to encode non-Markovian temporal…

Quantum Physics · Physics 2025-11-13 Jia-Bin You , Jian Feng Kong , Jun Ye

We describe a generic infrastructure for time evolution simulations in numerical relativity using multiple grid patches. After a motivation of this approach, we discuss the relative advantages of global and patch-local tensor bases. We…

General Relativity and Quantum Cosmology · Physics 2014-11-17 Erik Schnetter , Peter Diener , Ernst Nils Dorband , Manuel Tiglio

Global discrete optimization is notoriously difficult due to the lack of gradient information and the curse of dimensionality, making exhaustive search infeasible. Tensor cross approximation is an efficient technique to approximate…

Computation · Statistics 2025-02-19 Sergey Dolgov , Dmitry Savostyanov

We develop a computational framework that leverages the features of sophisticated software tools and numerics to tackle some of the pressing issues in the realm of earth sciences. The algorithms to handle the physics of multiphase flow,…

Computational Engineering, Finance, and Science · Computer Science 2021-02-10 Saumik Dana , Xiaoxi Zhao , Birendra Jha

We consider many-body effects on particle scattering in one, two and three dimensional Bose gases. We show that at zero temperature these effects can be modelled by the simpler two-body T-matrix evaluated off the energy shell. This is…

Condensed Matter · Physics 2009-11-07 M. D. Lee , S. A. Morgan , M. J. Davis , K. Burnett

Decompositions of tensors into factor matrices, which interact through a core tensor, have found numerous applications in signal processing and machine learning. A more general tensor model which represents data as an ordered network of…

Numerical Analysis · Computer Science 2016-09-30 Anh-Huy Phan , Andrzej Cichocki , Andre Uschmajew , Petr Tichavsky , George Luta , Danilo Mandic

Differential equations are important mechanistic models that are integral to many scientific and engineering applications. With the abundance of available data there has been a growing interest in data-driven physics-informed models.…

Machine Learning · Computer Science 2025-02-04 Oliver Hamelijnck , Arno Solin , Theodoros Damoulas

We generalize the interpolative separable density fitting (ISDF) method, used for compressing the four-index electron repulsion integral (ERI) tensor, to incorporate adaptive real space grids for potentially highly localized single-particle…

Computational Physics · Physics 2026-02-17 Hai Zhu , Chia-Nan Yeh , Miguel A. Morales , Leslie Greengard , Shidong Jiang , Jason Kaye

Additive Gaussian process (GP) models offer flexible tools for modelling complex non-linear relationships and interaction effects among covariates. While most studies have focused on predictive performance, relatively little attention has…

Methodology · Statistics 2025-10-30 Sahoko Ishida , Francesca Panero , Wicher Bergsma

Tensor network techniques have proved to be powerful tools that can be employed to explore the large scale dynamics of lattice systems. Nonetheless, the redundancy of degrees of freedom in lattice gauge theories (and related models) poses a…

General Relativity and Quantum Cosmology · Physics 2016-05-12 Bianca Dittrich , Sebastian Mizera , Sebastian Steinhaus

We present a general method for approximately contracting tensor networks with an arbitrary connectivity. This enables us to release the computational power of tensor networks to wide use in inference and learning problems defined on…

Computational Physics · Physics 2020-08-12 Feng Pan , Pengfei Zhou , Sujie Li , Pan Zhang

Computational modeling and simulation of fluid-structure interactions constitute a fundamental cornerstone for advancing aerospace engineering endeavors. This paper addresses the notion and implementation of the immersed boundary method for…

Computational Physics · Physics 2025-12-24 Longqing Ge , Qingdong Cai , Yonghao Zhang , Tianbai Xiao

Tensor network decompositions offer an efficient description of certain many-body states of a lattice system and are the basis of a wealth of numerical simulation algorithms. We discuss how to incorporate a global symmetry, given by a…

Strongly Correlated Electrons · Physics 2010-11-19 Sukhwinder Singh , Robert N. C. Pfeifer , Guifre Vidal

We present a numerical formulation for the solution of non-isothermal, compressible, Navier-Stokes equations with thermal fluctuations to describe mesoscale transport phenomena in multispecies fluid mixtures. The novelty of our numerical…

In this paper, we are concerned with the quantification of uncertainties that arise from intra-day oscillations in the demand for natural gas transported through large-scale networks. The short-term transient dynamics of the gas flow is…

Numerical Analysis · Mathematics 2020-12-08 Jens Lang , Pia Domschke , Elisa Strauch

Turbulence governs the transport of momentum, energy, and scalars in many geophysical and engineering flows. In large-eddy simulations (LES), parameterizing subgrid-scale (SGS) stresses remains a central challenge, as unresolved physical…

Fluid Dynamics · Physics 2025-10-02 Yu Cheng , Tianle Liu

We study the time-dependent Gross-Pitaevskii equation describing Bose-Einstein condensation of trapped dipolar quantum gases. Existence and uniqueness as well as the possible blow-up of solutions are studied. Moreover, we discuss the…

Mathematical Physics · Physics 2009-11-13 Rémi Carles , Peter Markowich , Christof Sparber

Turbulence cascade has been modeled using various methods; the one we have used applies to a more exact representation of turbulence where people use the multifractal representation. The nature of the energy dissipation is usually governed…

Fluid Dynamics · Physics 2024-12-31 Vicente Corral Arreola , Arturo Rodriguez , Vinod Kumar

The identification of nonlinear dynamics from observations is essential for the alignment of the theoretical ideas and experimental data. The last, in turn, is often corrupted by the side effects and noise of different natures, so…

Machine Learning · Computer Science 2020-06-08 Anna Shalova , Ivan Oseledets
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