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Dirac-delta distributions are often crucial components of the solid-fluid coupling operators in immersed solution methods for fluid-structure interaction (FSI) problems. This is certainly so for methods like the Immersed Boundary Method…

Numerical Analysis · Mathematics 2013-02-06 Luca Heltai , Francesco Costanzo

Gaussian process regression techniques have been used in fluid mechanics for the reconstruction of flow fields from a reduction-of-dimension perspective. A main ingredient in this setting is the construction of adapted covariance functions,…

Fluid Dynamics · Physics 2026-01-13 Adrian Padilla-Segarra , Pascal Noble , Olivier Roustant , Éric Savin

We present an improved method for computing incompressible viscous flow around suspended rigid particles using a fixed and uniform computational grid. The main idea is to incorporate Peskin's regularized delta function approach [Acta…

Fluid Dynamics · Physics 2018-09-24 Markus Uhlmann

The immersed boundary-finite element method (IBFE) is an approach to describing the dynamics of an elastic structure immersed in an incompressible viscous fluid. In this formulation, there are discontinuities in the pressure and viscous…

Numerical Analysis · Mathematics 2020-03-18 Charles Puelz , Boyce E. Griffith

Combining sum factorization, weighted quadrature, and row-based assembly enables efficient higher-order computations for tensor product splines. We aim to transfer these concepts to immersed boundary methods, which perform simulations on a…

Computational Engineering, Finance, and Science · Computer Science 2023-09-06 Benjamin Marussig , René Hiemstra , Dominik Schillinger

We apply the immersed boundary (or IB) method to simulate deformation and detachment of a periodic array of wall-bounded biofilm colonies in response to a linear shear flow. The biofilm material is represented as a network of Hookean…

Fluid Dynamics · Physics 2016-11-01 Rangarajan Sudarsan , Sudeshna Ghosh , John M. Stockie , Hermann J. Eberl

Kernel techniques are among the most popular and flexible approaches in data science allowing to represent probability measures without loss of information under mild conditions. The resulting mapping called mean embedding gives rise to a…

Machine Learning · Statistics 2024-11-27 Linda Chamakh , Zoltan Szabo

Machine learning (ML) entered the field of computational micromagnetics only recently. The main objective of these new approaches is the automatization of solutions of parameter-dependent problems in micromagnetism such as fast response…

Computational Physics · Physics 2021-07-15 Sebastian Schaffer , Norbert J. Mauser , Thomas Schrefl , Dieter Suess , Lukas Exl

Implicit neural representations have emerged as a powerful approach for encoding complex geometries as continuous functions. These implicit models are widely used in computer vision and 3D content creation, but their integration into…

We present a novel framework to deal with static and moving immersed boundaries (IB). In this strategy, called Volume-Filtering Immersed Boundary (VFIB) method, transport equations are derived by filtering the Navier-Stokes equations and…

Fluid Dynamics · Physics 2023-05-24 Himanshu Dave , Marcus Herrmann , M. Houssem Kasbaoui

An immersed-boundary (IB) method is proposed and applied in the gas-kinetic BGK scheme to simulate incompressible/compressible viscous flow with stationary/moving boundary. In present method the ghost-cell technique is adopted to fulfill…

Computational Physics · Physics 2016-11-07 Ruifeng Yuan , Chengwen Zhong

A novel sharp interface ghost-cell based immersed boundary method has been proposed and its parameters have been optimized against an analytical model in diffusion applications. The proposed embedded constrained moving least-squares (ECMLS)…

Numerical Analysis · Mathematics 2021-04-01 Andrew C. Chiang , Po-Nan Li , Soichi Wakatsuki

This paper presents an immersed boundary (IB) method for fluid--structure--acoustics interactions involving large deformations and complex geometries. In this method, the fluid dynamics is solved by a finite difference method where the…

Fluid Dynamics · Physics 2020-05-20 Li Wang , Fang-Bao Tian , Joseph C. S. Lai

In our recent work [H. Zhang, F.X. Trias, A. Oliva, D. Yang, Y. Tan, Y. Sheng. PIBM: Particulate immersed boundary method for fluid-particle interaction problems. Powder Technology. 272(2015), 1-13.], a particulate immersed boundary method…

Computational Physics · Physics 2015-02-05 Hao Zhang , Haizhuan Yuan , F. Xavier Trias , Aibing Yu , Yuanqiang Tan , Assensi Oliva

The Information Bottleneck (IB) is a conceptual method for extracting the most compact, yet informative, representation of a set of variables, with respect to the target. It generalizes the notion of minimal sufficient statistics from…

Machine Learning · Computer Science 2017-11-08 Amichai Painsky , Naftali Tishby

The lattice Boltzmann method has become a widely adopted approach in computational fluid dynamics, offering unique advantages in mesoscopic kinetic modeling, intrinsic parallelism, and simple treatment of boundary conditions. However, its…

Numerical Analysis · Mathematics 2025-09-16 Ye Ji , Monica Lacatus , Matthias Möller

We present a novel kernel-based machine learning algorithm for identifying the low-dimensional geometry of the effective dynamics of high-dimensional multiscale stochastic systems. Recently, the authors developed a mathematical framework…

Dynamical Systems · Mathematics 2020-02-04 Andreas Bittracher , Stefan Klus , Boumediene Hamzi , Péter Koltai , Christof Schütte

The immersed isogeometric Boundary Element Method is presented and applied to the simulation of underground excavations. Nonuniform rational B-splines (NURBS) are used for the accurate definition of complex geometries with few parameters.…

Numerical Analysis · Mathematics 2022-12-01 Gernot Beer , Christian Duenser

We present an efficient and accurate immersed boundary (IB) finite element (FE) solver for numerically solving incompressible Navier--Stokes equations. Particular emphasis is given to internal flows with complex geometries (blood flow in…

Computational Engineering, Finance, and Science · Computer Science 2020-07-07 G. C. Bourantas , D. L. Lampropoulos , B. F. Zwick , V. C. Loukopoulos , A. Wittek , K. Miller

Machine Learning (ML) has widely been used for modeling and predicting physical systems. These techniques offer high expressive power and good generalizability for interpolation within observed data sets. However, the disadvantage of…

Machine Learning · Statistics 2023-03-02 Omid Sedehi , Antonina M. Kosikova , Costas Papadimitriou , Lambros S. Katafygiotis