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We study network design problems for nonlinear and nonconvex flow models without controllable elements under load scenario uncertainties, i.e., under uncertain injections and withdrawals. To this end, we apply the concept of adjustable…

Optimization and Control · Mathematics 2025-01-20 Johannes Thürauf , Julia Grübel , Martin Schmidt

This work concerns the numerical approximation of a multicomponent compressible Euler system for a fluid mixture in multiple space dimensions on unstructured meshes with a high-order discontinuous Galerkin spectral element method (DGSEM).…

Numerical Analysis · Mathematics 2021-08-25 Florent Renac

We present an adaptive reduced-order model for the efficient time-resolved simulation of fluid-structure interaction problems with complex and non-linear deformations. The model is based on repeated linearizations of the structural balance…

Fluid Dynamics · Physics 2020-04-13 Ali Thari , Vito Pasquariello , Niels Aage , Stefan Hickel

In recent years, the study of biological transportation networks has attracted significant interest, focusing on their self-regulating, demand-driven nature. This paper examines a mathematical model for these networks, featuring nonlinear…

Analysis of PDEs · Mathematics 2024-08-29 Clarissa Astuto , Peter Markowich , Simone Portaro , Giovanni Russo

We discuss the efficient implementation of a high-performance second-order collocation-type finite-element scheme for solving the compressible Euler equations of gas dynamics on unstructured meshes. The solver is based on the convex…

Mathematical Software · Computer Science 2021-02-04 Matthias Maier , Martin Kronbichler

We present a novel fully implicit hybrid finite volume/finite element method for incompressible flows. Following previous works on semi-implicit hybrid FV/FE schemes, the incompressible Navier-Stokes equations are split into a pressure and…

Numerical Analysis · Mathematics 2023-02-14 Alessia Lucca , Saray Busto , Michael Dumbser

We introduce an algorithmic framework based on tensor networks for computing fluid flows around immersed objects in curvilinear coordinates. We show that the tensor network simulations can be carried out solely using highly compressed…

This paper studies the characteristics and applicability of the CutFEM approach as the core of a robust topology optimization framework for 3D laminar incompressible flow and species transport problems at low Reynolds number (Re < 200).…

Optimization and Control · Mathematics 2017-02-09 Hernan Villanueva , Kurt Maute

We prove the well-posedness of entropy solutions for a wide class of nonlocal transport equations with nonlinear mobility in one spatial dimension. The solution is obtained as the limit of approximations constructed via a deterministic…

Analysis of PDEs · Mathematics 2025-09-25 Simone Fagioli , Oliver Tse

We investigate a scalar partial differential equation model for the formation of biological transportation networks. Starting from a discrete graph-based formulation on equilateral triangulations, we rigorously derive the corresponding…

Analysis of PDEs · Mathematics 2025-10-20 Jan Haskovec , Peter Markowich , Stefano Zampini

In this paper we propose a novel and general approach to design semi-implicit methods for the simulation of fluid-structure interaction problems in a fully Eulerian framework. In order to properly present the new method, we focus on the…

Numerical Analysis · Mathematics 2023-10-31 Mirco Ciallella , Thomas Milcent

The paper develops a solver based on a conforming finite element method for a 3D--1D coupled incompressible flow problem. New coupling conditions are introduced to ensure a suitable bound for the cumulative energy of the model. We study the…

Computational Physics · Physics 2013-04-08 Tatiana K. Dobroserdova , Maxim A. Olshanskii

This work gathers new results concerning the semi-geostrophic equations: existence and stability of measure valued solutions, existence and uniqueness of solutions under certain continuity conditions for the density, convergence to the…

Analysis of PDEs · Mathematics 2007-05-23 G. Loeper

We study the implicit bias of gradient flow (i.e., gradient descent with infinitesimal step size) on linear neural network training. We propose a tensor formulation of neural networks that includes fully-connected, diagonal, and…

Machine Learning · Computer Science 2021-09-13 Chulhee Yun , Shankar Krishnan , Hossein Mobahi

We present an efficient discontinuous Galerkin scheme for simulation of the incompressible Navier-Stokes equations including laminar and turbulent flow. We consider a semi-explicit high-order velocity-correction method for time integration…

Numerical Analysis · Mathematics 2017-08-15 Benjamin Krank , Niklas Fehn , Wolfgang A. Wall , Martin Kronbichler

Fluid flows are omnipresent in nature and engineering disciplines. The reliable computation of fluids has been a long-lasting challenge due to nonlinear interactions over multiple spatio-temporal scales. The compressible Navier-Stokes…

Fluid Dynamics · Physics 2021-12-10 Deniz A. Bezgin , Aaron B. Buhendwa , Nikolaus A. Adams

We propose a low-rank tensor approach to approximate linear transport and nonlinear Vlasov solutions and their associated flow maps. The approach takes advantage of the fact that the differential operators in the Vlasov equation is tensor…

Numerical Analysis · Mathematics 2022-03-23 Wei Guo , Jing-Mei Qiu

We study several iterative methods for fully coupled flow and reactive transport in porous media. The resulting mathematical model is a coupled, nonlinear evolution system. The flow model component builds on the Richards equation, modified…

Numerical Analysis · Mathematics 2021-01-01 Davide Illiano , Jakub Wiktor Both , Iuliu Sorin Pop , Florin Adrian Radu

Efficient simulation of the Navier-Stokes equations for fluid flow is a long standing problem in applied mathematics, for which state-of-the-art methods require large compute resources. In this work, we propose a data-driven approach that…

Computer Vision and Pattern Recognition · Computer Science 2022-11-10 Jonathan Tompson , Kristofer Schlachter , Pablo Sprechmann , Ken Perlin

We propose a non-intrusive, Autoencoder-based framework for reduced-order modeling in continuum mechanics. Our method integrates three stages: (i) an unsupervised Autoencoder compresses high-dimensional finite element solutions into a…

Computational Engineering, Finance, and Science · Computer Science 2025-09-03 Jannick Kehls , Ellen Kuhl , Tim Brepols , Kevin Linka , Hagen Holthusen
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