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We use machine learning methods on local structure to identify flow defects - or regions susceptible to rearrangement - in jammed and glassy systems. We apply this method successfully to two disparate systems: a two dimensional experimental…

A defect-deferred correction method, increasing both temporal and spatial accuracy, for fluid-fluid interaction problem with nonlinear interface condition is considered by geometric averaging of the previous two-time levels. In the defect…

Numerical Analysis · Mathematics 2020-06-02 Mustafa Aggul , Songül Kaya

We study different dimensional fluids inspired by noncommutative geometry which admit conformal Killing vectors. The solutions of the Einstein field equations examined specifically for five different set of spacetime. We calculate the…

General Relativity and Quantum Cosmology · Physics 2015-04-15 Farook Rahaman , Anirudh Pradhan , Nasr Ahmed , Saibal Ray , Bijan Saha , Mosiur Rahaman

Extracting information on fluid motion directly from images is challenging. Fluid flow represents a complex dynamic system governed by the Navier-Stokes equations. General optical flow methods are typically designed for rigid body motion,…

Machine Learning · Computer Science 2022-06-23 Mingrui Zhang , Jianhong Wang , James Tlhomole , Matthew D. Piggott

The gauge invariant electromagnetic Wigner equation is taken as the basis for a fluid-like system describing quantum plasmas, derived from the moments of the gauge invariant Wigner function. The use of the standard, gauge dependent Wigner…

Quantum Physics · Physics 2015-05-14 F. Haas , J. Zamanian , M. Marklund , G. Brodin

In addition to providing high-profile successes in computer vision and natural language processing, neural networks also provide an emerging set of techniques for scientific problems. Such data-driven models, however, typically ignore…

Computational Physics · Physics 2019-05-28 N. Benjamin Erichson , Michael Muehlebach , Michael W. Mahoney

The development of the classical physics fundamentals with usage of experience of the experimental and computational-analytical investigations has allowed to create the valid conception of a fluid motion. The multidisciplinary approach on a…

Fluid Dynamics · Physics 2007-05-23 S. L. Arsenjev , I. B. Lozovitski , Y. P. Sirik

The inverse problem is studied in multi-body systems with nonlinear dynamics representing, e.g., phase-locked wave systems, standard multimode and random lasers. Using a general model for four-body interacting complex-valued variables we…

Disordered Systems and Neural Networks · Physics 2017-08-01 Alessia Marruzzo , Payal Tyagi , Fabrizio Antenucci , Andrea Pagnani , Luca Leuzzi

Glass transitions are widely observed in various types of soft matter systems. However, the physical mechanism of these transitions remains {elusive}, despite years of ambitious research. In particular, an important unanswered question is…

Disordered Systems and Neural Networks · Physics 2022-12-20 Norihiro Oyama , Shihori Koyama , Takeshi Kawasaki

The subject of this work is the instability mechanism of simple shear flows, like Hagen-Poiseuille pipe flow, which is a long-standing problem in fluid mechanics [1,2]. A possible analogy with phenomenological theory of ideal plasticity in…

Fluid Dynamics · Physics 2007-05-23 Sergey Ananiev

In the laminar mode interactions among molecules generate friction between layers of water that slide with respect to each other. This friction triggers the shear stress, which is traditionally presumed to be linearly proportional to the…

Fluid Dynamics · Physics 2012-06-21 K. Y. Volokh

We present efficient deep learning techniques for approximating flow and transport equations for both single phase and two-phase flow problems. The proposed methods take advantages of the sparsity structures in the underlying discrete…

Numerical Analysis · Mathematics 2020-01-08 Yating Wang , Guang Lin

We consider two-level finite element discretization methods for the stream function formulation of the Navier-Stokes equations. The two-level method consists of solving a small nonlinear system on the coarse mesh, then solving a linear…

Numerical Analysis · Mathematics 2025-10-20 Faisal Fairag

In this work we study the coupled system of partial and ordinary differential equations describing the interaction between a compressible isentropic viscous fluid and a rigid body moving freely inside the fluid. In particular the position…

Analysis of PDEs · Mathematics 2019-05-27 Ondrej Kreml , Sarka Necasova , Tomasz Piasecki

We consider the Navier-Stokes system solution, based at parametric representation of desired function. This solution is unique and it show the velocity of a stream element as its density structure [{\rho}_S (x,y,z,t);{\rho}^\to_L (x,y,z,t)]…

Mathematical Physics · Physics 2018-11-21 Alexandr Fridrikson , Marina Kasatochkina

This paper presents a numerical method for the simulation of fluid-structure interaction specifically tailored to interactions between Newtonian fluids and a large number of slender viscoelastic Cosserat rods. Because of their high…

Soft Condensed Matter · Physics 2022-01-13 S. Tschisgale , J. Fröhlich

Simulating physically plausible trajectories toward user-defined goals is a fundamental yet challenging task in fluid dynamics. While particle-based simulators can efficiently reproduce forward dynamics, inverse inference remains difficult,…

Machine Learning · Computer Science 2025-09-29 Mu Huang , Linning Xu , Mingyue Dai , Yidi Shao , Bo Dai

Physically plausible fluid simulations play an important role in modern computer graphics and engineering. However, in order to achieve real-time performance, computational speed needs to be traded-off with physical accuracy. Surrogate…

Fluid Dynamics · Physics 2021-05-19 Nils Wandel , Michael Weinmann , Reinhard Klein

We advocate for a more stringent test problem for codes that aim to solve the equations of viscous hydrodynamics. Specifically, we discuss a nonuniform-density version of the common (uniform-density) Gaussian velocity shear test, where…

Fluid Dynamics · Physics 2026-03-04 Alexander J. Dittmann , Geoffrey Ryan

The distinctive architectural features of normalizing flows (NFs), notably bijectivity and tractable Jacobians, make them well-suited for generative modeling. Invertible neural networks (INNs) build on these principles to address supervised…

Machine Learning · Computer Science 2026-02-25 Shubhanshu Shekhar , Mohammad Javad Khojasteh , Ananya Acharya , Tony Tohme , Kamal Youcef-Toumi