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In compressible fluid flow, reconstructing shocks, discontinuities, rarefactions, and their interactions from sparse measurements is an important inverse problem with practical applications. Moreover, physics-informed machine learning has…

Fluid Dynamics · Physics 2025-05-28 Rami Cassia , Rich Kerswell

High-fidelity, high-resolution numerical simulations are crucial for studying complex multiscale phenomena in fluid dynamics, such as turbulent flows and ocean waves. However, direct numerical simulations with high-resolution solvers are…

Numerical Analysis · Mathematics 2025-04-14 Wuzhe Xu , Yulong Lu , Lian Shen , Anqing Xuan , Ali Barzegari

The Riemann problem for first-order hyperbolic systems of partial differential equations is of fundamental importance for both theoretical and numerical purposes. Many approximate solvers have been developed for such systems; exact solution…

Numerical Analysis · Mathematics 2024-02-22 Carlos Muñoz Moncayo , Manuel Quezada de Luna , David I. Ketcheson

We investigate various analytical and numerical techniques for the coupling of nonlinear hyperbolic systems and, in particular, we introduce here an augmented formulation which allows for the modeling of the dynamics of interfaces between…

Analysis of PDEs · Mathematics 2021-10-01 Benjamin Boutin , Frédéric Coquel , Philippe G. LeFloch

The parallel solver of the general synthetic iterative scheme (GSIS), as recently developed by Zhang \textit{et. al.} in Comput. Fluids 281 (2024) 106374, is an efficient method to find the solution of the Boltzmann equation…

Computational Physics · Physics 2025-01-07 Yanbing Zhang , Jianan Zeng , Lei Wu

We have generalised the exact solution of the Riemann problem in special relativistic hydrodynamics for arbitrary tangential flow velocities. The solution is obtained by solving the jump conditions across shocks plus an ordinary…

Astrophysics · Physics 2007-05-23 J. Pons , J. Ma. Marti , E. Muller

Such problems as computation of spectra of spin chains and vibrational spectra of molecules can be written as high-dimensional eigenvalue problems, i.e., when the eigenvector can be naturally represented as a multidimensional tensor. Tensor…

Numerical Analysis · Mathematics 2019-09-04 Maxim Rakhuba , Alexander Novikov , Ivan Oseledets

We present an $rp$-adaptation strategy for high-fidelity simulation of compressible inviscid flows with shocks. The mesh resolution in regions of flow discontinuities is increased by using a variational optimiser to $r$-adapt the mesh and…

Computational Physics · Physics 2020-11-10 Julian Marcon , Giacomo Castiglioni , David Moxey , Spencer J. Sherwin , Joaquim Peiró

We present a detailed solution of the active interface equations in the inviscid limit. The active interface equations were previously introduced as a toy model of membrane-protein systems: they describe a stochastic interface where growth…

Statistical Mechanics · Physics 2019-12-09 Francesco Cagnetta , Martin R. Evans

Adaptive mesh refinement (AMR) is a classical technique about local refinement in space where needed, thus effectively reducing computational costs for HPC-based physics simulations. Although AMR has been used for many years, little…

Fluid Dynamics · Physics 2024-05-14 Dewen Liu , Shuai He , Haoran Cheng , Yadong Zeng

Generative models have emerged as a powerful paradigm for solving physics systems and modeling complex spatiotemporal dynamics. However, achieving high physical accuracy without incurring high computational cost remains a fundamental…

Machine Learning · Computer Science 2026-05-27 Jiahe Huang , Sihan Xu , Sharvaree Vadgama , Rose Yu

Flows in which the primary features of interest do not rely on high-frequency acoustic effects, but in which long-wavelength acoustics play a nontrivial role, present a computational challenge. Integrating the entire domain with…

Numerical Analysis · Mathematics 2018-08-09 Emmanuel Motheau , Max Duarte , Ann Almgren , John B. Bell

Recent studies have advocated using the total dissipation rate under topology optimization to realize material designs involving the flow of fluids through porous media. However, these studies decided how to pose the design problem, such as…

Numerical Analysis · Mathematics 2022-02-16 K. B. Nakshatrala

We show feasibility and benefits of porting an adaptive multi-scale kinetic-fluid code to CPU-GPU systems. Challenges are due to the irregular data access for adaptive Cartesian mesh, vast difference of computational cost between kinetic…

Computational Physics · Physics 2015-07-23 Sergey Zabelok , Robert Arslanbekov , Vladimir Kolobov

Essential features of the Multigrid Ensemble Kalman Filter (G. Moldovan, G. Lehnasch, L. Cordier, M. Meldi, A multigrid/ensemble Kalman filter strategy for assimilation of unsteady flows, Journal of Computational Physics 443-110481)…

Fluid Dynamics · Physics 2022-10-20 Gabriel Moldovan , Guillaume Lehnasch , Laurent Cordier , Marcello Meldi

We present a method for computing fluid-structure interaction problems for multi-body systems. The fluid flow equations are solved using a fractional-step method with the immersed boundary method proposed by Uhlmann [J. Comput Phys. 209…

We present novel coupling schemes for partitioned multi-physics simulation that combine four important aspects for strongly coupled problems: implicit coupling per time step, fast and robust acceleration of the corresponding iterative…

Numerical Analysis · Mathematics 2020-09-21 Benjamin Rüth , Benjamin Uekermann , Miriam Mehl , Philipp Birken , Azahar Monge , Hans-Joachim Bungartz

The solution of an extended Riemann problem is used to provide the internal boundary conditions at a junction when simulating one-dimensional flow through an open channel network. The proposed approach, compared to classic junction models,…

Fluid Dynamics · Physics 2019-12-16 Mohamed Elshobaki , Alessandro Valiani , Valerio Caleffi

Computational Fluid Dynamics (CFD) simulations are often constrained by the memory-bound nature of sparse matrix-vector operations, which eventually limits performance on modern high-performance computing (HPC) systems. This work introduces…

We propose Riemannian Flow Matching (RFM), a simple yet powerful framework for training continuous normalizing flows on manifolds. Existing methods for generative modeling on manifolds either require expensive simulation, are inherently…

Machine Learning · Computer Science 2024-02-27 Ricky T. Q. Chen , Yaron Lipman