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An a priori reduced order method based on the proper generalised decomposition (PGD) is proposed to compute parametric solutions involving turbulent incompressible flows of interest in an industrial context, using OpenFOAM. The PGD…

Computational Physics · Physics 2021-11-16 Vasileios Tsiolakis , Matteo Giacomini , Ruben Sevilla , Carsten Othmer , Antonio Huerta

In order to improve the application maturity of high-order difference schemes, the free-stream preservation property, whose importance has been widely recognized in recent years, has been developed into a focus of study.. In past…

Fluid Dynamics · Physics 2016-02-03 Qin Li , Dong Sun , Hanxin Zhang

We study image inverse problems with a normalizing flow prior. Our formulation views the solution as the maximum a posteriori estimate of the image conditioned on the measurements. This formulation allows us to use noise models with…

Machine Learning · Computer Science 2021-07-02 Jay Whang , Qi Lei , Alexandros G. Dimakis

We propose a novel framework for approximating the statistical properties of turbulent flows by combining variational methods for the search of unstable periodic orbits with resolvent analysis for dimensionality reduction. Traditional…

Chaotic Dynamics · Physics 2025-01-22 Thomas Burton , Sean Symon , Ati Sharma , Davide Lasagna

While deep learning-based super-resolution (SR) methods have shown impressive outcomes with synthetic degradation scenarios such as bicubic downsampling, they frequently struggle to perform well on real-world images that feature complex,…

Computer Vision and Pattern Recognition · Computer Science 2026-02-05 Hyeonjae Kim , Dongjin Kim , Eugene Jin , Tae Hyun Kim

This paper presents a numerical method to implement the parameter estimation method using response statistics that was recently formulated by the authors. The proposed approach formulates the parameter estimation problem of It\^o drift…

Numerical Analysis · Mathematics 2019-03-05 He Zhang , Xiantao Li , John Harlim

Measurements acquired from distributed physical systems are often sparse and noisy. Therefore, signal processing and system identification tools are required to mitigate noise effects and reconstruct unobserved dynamics from limited sensor…

Machine Learning · Computer Science 2025-09-08 Omid Sedehi , Manish Yadav , Merten Stender , Sebastian Oberst

Analysing data from Smoothed Particle Hydrodynamics (SPH) simulations is about understanding global fluid properties rather than individual fluid elements. Therefore, in order to properly understand the outcome of such simulations it is…

Instrumentation and Methods for Astrophysics · Physics 2018-03-13 Bernhard Röttgers , Alexander Arth

Flow based models such as Real NVP are an extremely powerful approach to density estimation. However, existing flow based models are restricted to transforming continuous densities over a continuous input space into similarly continuous…

Machine Learning · Computer Science 2020-08-27 Laurent Dinh , Jascha Sohl-Dickstein , Hugo Larochelle , Razvan Pascanu

A stabilized finite element method is introduced for the simulation of time-periodic creeping flows, such as those found in the cardiorespiratory systems. The new technique, which is formulated in the frequency rather than time domain,…

Numerical Analysis · Mathematics 2022-11-30 Mahdi Esmaily

When simulating multiscale systems, where some fields cannot be fully prescribed despite their effects on the simulation's accuracy, closure models are needed. This phenomenon is observed in turbulent fluid dynamics, where Large Eddy…

Fluid Dynamics · Physics 2025-12-01 Eduardo Vital , Jean-Marc Gratien , Yassine Ayoun , Thibault Faney , Julien Bohbot

We investigate a new two-dimensional compressible Navier-Stokes hydrodynamic model design to explain and study large scale ice swirls formation at the surface of the ocean. The linearized model generates a basis of Bessel solutions from…

Pattern Formation and Solitons · Physics 2019-07-24 Zhi Zong , Andrei Ludu

Normalizing flows (NFs) have become a prominent method for deep generative models that allow for an analytic probability density estimation and efficient synthesis. However, a flow-based network is considered to be inefficient in parameter…

Machine Learning · Computer Science 2020-10-26 Sang-gil Lee , Sungwon Kim , Sungroh Yoon

The control of nonlinear large-scale dynamical models such as the incompressible Navier-Stokes equations is a challenging task. The computational challenges in the controller design come from both the possibly large state space and the…

Optimization and Control · Mathematics 2023-11-10 Amritam Das , Jan Heiland

We study the sub-grid scale characteristics of a vorticity-transport-based approach for large-eddy simulations. In particular, we consider a multi-dimensional upwind scheme for the vorticity transport equations and establish its properties…

Fluid Dynamics · Physics 2021-02-05 Daniel Foti , Karthik Duraisamy

Knowledge of the bottom topography, also called bathymetry, of rivers, seas or the ocean is important for many areas of maritime science and civil engineering. While direct measurements are possible, they are time consuming and expensive.…

Numerical Analysis · Mathematics 2024-06-03 Judith Angel , Jörn Behrens , Sebastian Götschel , Marten Hollm , Daniel Ruprecht , Robert Seifried

In recent work, the authors have developed a generic methodology for calibrating the noise in fluid dynamics stochastic partial differential equations where the stochasticity was introduced to parametrize subgrid-scale processes. The…

Machine Learning · Statistics 2024-03-19 Dan Crisan , Oana Lang , Alexander Lobbe

We present an estimator-based control design procedure for flow control, using reduced-order models of the governing equations, linearized about a possibly unstable steady state. The reduced models are obtained using an approximate balanced…

Optimization and Control · Mathematics 2015-05-13 Sunil Ahuja , Clarence W. Rowley

We propose an extension of the discretization approaches for multilayer shallow water models, aimed at making them more flexible and efficient for realistic applications to coastal flows. A novel discretization approach is proposed, in…

Numerical Analysis · Mathematics 2018-04-18 Luca Bonaventura , Enrique D. Fernández-Nieto , José Garres-Díaz , Gladys Narbona-Reina

Two-scale models pose a promising approach in simulating reactive flow and transport in evolving porous media. Classically, homogenized flow and transport equations are solved on the macroscopic scale, while effective parameters are…

Analysis of PDEs · Mathematics 2022-02-01 Stephan Gärttner , Peter Knabner , Nadja Ray
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