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Generative models based on dynamical equations such as flows and diffusions offer exceptional sample quality, but require computationally expensive numerical integration during inference. The advent of consistency models has enabled…

Machine Learning · Computer Science 2025-06-04 Nicholas M. Boffi , Michael S. Albergo , Eric Vanden-Eijnden

In this paper, we present a novel general framework grounded in the factor graph theory to solve kinematic and dynamic problems for multi-body systems. Although the motion of multi-body systems is considered to be a well-studied problem and…

Robotics · Computer Science 2021-07-27 José-Luis Blanco-Claraco , Antonio Leanza , Giulio Reina

Accurate beam alignment is a critical challenge in XL-MIMO systems, especially in the near-field regime, where conventional far-field assumptions no longer hold. Although 2D grid-based codebooks in the polar domain are widely accepted for…

Signal Processing · Electrical Eng. & Systems 2025-02-27 Hongjun Heo , Wan Choi

Reduced Differental Transform Method (RDTM) which is one of the useful and effective numerical approximate method is applied to solve nonlinear time-dependent Foam Drainage Equation (FDE). Also, we compared the presented method with the…

Numerical Analysis · Mathematics 2013-11-26 Murat Gubes

Most of the FFT methods available for homogenization of the mechanical response use the strain/deformation gradient as unknown, imposing their compatibility using Green's functions or projection operators. This implies the allocation of…

Computational Engineering, Finance, and Science · Computer Science 2019-08-27 Sergio Lucarini , Javier Segurado

High mobility environment leads to severe Doppler effects and poses serious challenges to the conventional physical layer based on the widely popular orthogonal frequency division multiplexing (OFDM). The recent emergence of orthogonal time…

Signal Processing · Electrical Eng. & Systems 2025-02-14 Qinwen Deng , Yao Ge , Zhi Ding

Fuzzy Cognitive Maps (FCMs) are considered a soft computing technique combining elements of fuzzy logic and recurrent neural networks. They found multiple application in such domains as modeling of system behavior, prediction of time…

Machine Learning · Computer Science 2021-03-16 Piotr Szwed

General Matrix Multiplication (GEMM) has a wide range of applications in scientific simulation and artificial intelligence. Although traditional libraries can achieve high performance on large regular-shaped GEMMs, they often behave not…

Distributed, Parallel, and Cluster Computing · Computer Science 2022-08-12 Shangfei Yin , Qinglin Wang , Ruochen Hao , Tianyang Zhou , Songzhu Mei , Jie Liu

The current fabrication and assembly of fluidic circuits for soft robots relies heavily on manual processes; as the complexity of fluidic circuits increases, manual assembly becomes increasingly arduous, error-prone, and timeconsuming. We…

Robotics · Computer Science 2023-12-05 Lehong Wang , Savita V. Kendre , Haotian Liu , Markus P. Nemitz

This paper introduces the FEDM (Finite Element Discharge Modelling) code, which was developed using the open-source computing platform FEniCS (https://fenicsproject.org). Building on FEniCS, the FEDM code utilises the finite element method…

Plasma Physics · Physics 2023-03-27 Aleksandar P. Jovanović , Detlef Loffhagen , Markus M. Becker

The rise of foundation models (FMs) has reshaped the landscape of machine learning. As these models continued to grow, leveraging geo-distributed data from wireless devices has become increasingly critical, giving rise to federated…

Turbulent flows and fluid-structure interactions (FSI) are ubiquitous in scientific and engineering applications, but their accurate and efficient simulation remains a major challenge due to strong nonlinearities, multiscale interactions,…

Fluid Dynamics · Physics 2025-06-02 Xiantao Fan , Xinyang Liu , Meng Wang , Jian-Xun Wang

Motivated by the looming "capacity crunch" in fiber-optic networks, information transmission over such systems is revisited. Among numerous distortions, inter-channel interference in multiuser wavelength-division multiplexing (WDM) is…

Information Theory · Computer Science 2014-10-09 Mansoor I. Yousefi , Frank R. Kschischang

Filter bank-based multicarrier (FBMC) systems have attracted increasing attention recently in view of their many advantages over the classical cyclic prefix (CP)-based orthogonal frequency division multiplexing (CP-OFDM) modulation.…

Signal Processing · Electrical Eng. & Systems 2020-07-15 Eleftherios Kofidis

In this paper, we propose a multiscale method for the Darcy-Forchheimer model in highly heterogeneous porous media. The problem is solved in the framework of generalized multiscale finite element methods (GMsFEM) combined with a multipoint…

Numerical Analysis · Mathematics 2020-07-20 Zhengkang He , Eric T. Chung , Jie Chen , Zhangxin Chen

We present Advancing Front Mapping (AFM), a provably robust algorithm for the computation of surface mappings to simple base domains. Given an input mesh and a convex or star-shaped target domain, AFM installs a (possibly refined) version…

Computational Geometry · Computer Science 2024-01-08 Marco Livesu

A nonlinear multigrid solver for two-phase flow and transport in a mixed fractional-flow velocity-pressure-saturation formulation is proposed. The solver, which is under the framework of the full approximation scheme (FAS), extends our…

Numerical Analysis · Mathematics 2021-09-17 Chak Shing Lee , François P. Hamon , Nicola Castelletto , Panayot S. Vassilevski , Joshua A. White

Fourier-domain Difference Map (FDM) for phase retrieval with two oversampled coded diffraction patterns are proposed. FDM is a 3-parameter family of fixed point algorithms including Fourier-domain Hybrid-Projection-Reflection (FHPR) and…

Data Analysis, Statistics and Probability · Physics 2016-03-09 Albert Fannjiang

Lattice Boltzmann Models (LBM) and Phase Field Models (PFM) are two of the most widespread approaches for the numerical study of multicomponent fluid systems. Both methods have been successfully employed by several authors but, despite…

Computational Physics · Physics 2012-09-21 Luca Scarbolo , Dafne Molin , Prasad Perlekar , Mauro Sbragaglia , Alfredo Soldati , Federico Toschi

Data-driven modeling of constrained multibody dynamics remains challenged by (i) the training cost of Neural ODEs, which typically require backpropagation through an ODE solver, and (ii) error accumulation in rollout predictions. We…

Machine Learning · Computer Science 2026-03-23 Hongyu Wang , Jingquan Wang , Dan Negrut