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We develop a new class of physics-informed neural network approximations for the stationary Oseen equations based on stability-consistent loss constructions. In contrast to standard PINN formulations, which are typically heuristic, the…

Numerical Analysis · Mathematics 2026-05-07 Shiv Mishra , Arbaz Khan

We present a shock capturing method for large-eddy simulation of turbulent flows. The proposed method relies on physical mechanisms to resolve and smooth sharp unresolved flow features that may otherwise lead to numerical instability, such…

Computational Physics · Physics 2018-06-19 Pablo Fernandez , Ngoc-Cuong Nguyen , Jaime Peraire

In this article, we improve the classical Bukhgeim-Klibanov method presented in [1],which can be used to prove the conditional stability of inverse source problem for a hyperbolic equation from the measurement on the subboundary. A major…

Analysis of PDEs · Mathematics 2026-03-27 Suliang Si

This work establishes nonlinear orbital asymptotic stability of scalar radiative shock profiles, namely, traveling wave solutions to the simplified model system of radiating gas \cite{Hm}, consisting of a scalar conservation law coupled…

Analysis of PDEs · Mathematics 2009-05-28 Corrado Lattanzio , Corrado Mascia , Ramon Plaza , Toan Nguyen , Kevin Zumbrun

In this paper, we investigate the inverse problem on determining the spatial component of the source term in a hyperbolic equation with time-dependent principal part. Based on a newly established Carleman estimate for general hyperbolic…

Analysis of PDEs · Mathematics 2019-04-12 Daijun Jiang , Yikan Liu , Masahiro Yamamoto

In this paper, we present a shock capturing discontinuous Galerkin (SC-DG) method for nonlinear systems of conservation laws in several space dimensions and analyze its stability and convergence. The scheme is realized as a space-time…

Numerical Analysis · Mathematics 2016-05-23 Mohammad Zakerzadeh , Georg May

The rise of Physics-Informed Neural Networks (PINNs) has popularized the concept of solving differential equations via residual minimization. However, neural networks are often viewed as ``black boxes" requiring significant computational…

Numerical Analysis · Mathematics 2026-05-20 Ayman Mourad , Fatima Mroue

We propose a novel projection method that guarantees the conservation of integral quantities in Physics-Informed Neural Networks (PINNs). While the soft constraint that PINNs use to enforce the structure of partial differential equations…

Machine Learning · Computer Science 2026-05-26 Anthony Baez , Wang Zhang , Ziwen Ma , Lam Nguyen , Subhro Das , Luca Daniel

In this work we consider brightness and mass conservation laws for motion estimation on evolving Riemannian 2-manifolds that allow for a radial parametrisation from the 2-sphere. While conservation of brightness constitutes the foundation…

Optimization and Control · Mathematics 2018-05-04 Lukas F. Lang

Neural networks have emerged as powerful surrogates for solving partial differential equations (PDEs), offering significant computational speedups over traditional methods. However, these models suffer from a critical limitation: error…

Machine Learning · Computer Science 2025-12-29 Xinquan Huang , Paris Perdikaris

We propose a physics-informed consistency modeling framework for solving partial differential equations (PDEs) via fast, few-step generative inference. We identify a key stability challenge in physics-constrained consistency training, where…

Machine Learning · Computer Science 2026-02-11 Che-Chia Chang , Chen-Yang Dai , Te-Sheng Lin , Ming-Chih Lai , Chieh-Hsin Lai

In this paper, we develop a new mass conservative numerical scheme for the simulations of a class of fluid-structure interaction problems. We will use the immersed boundary method to model the fluid-structure interaction, while the fluid…

Numerical Analysis · Mathematics 2018-06-21 Siu Wun Cheung , Eric Chung , Hyea Hyun Kim

Smoothed Particle Hydrodynamics (SPH) is a ubiquitous numerical method for solving the fluid equations, and is prized for its conservation properties, natural adaptivity, and simplicity. We introduce the Sphenix SPH scheme, which was…

Astrophysics of Galaxies · Physics 2021-11-17 Josh Borrow , Matthieu Schaller , Richard G. Bower , Joop Schaye

High-order moving-mesh methods can effectively reduce numerical diffusion, but their formal accuracy typically relies on the regularity of the mesh velocity. This dependency creates a fundamental conflict in the numerical solution of…

Numerical Analysis · Mathematics 2026-02-11 Chaoyi Cai , Qiqin Cheng , Di Wu , Jianxian Qiu

Shock wave reflection from a rigid wall has been thoroughly studied in the Newtonian limit, simplifying the problem by analyzing it in a steady-state frame, $S'$, where the point $P$ of the shock's intersection with the wall is at rest.…

High Energy Astrophysical Phenomena · Physics 2023-09-25 Jonathan Granot , Michael Rabinovich

By means of an original approach, called "method of the moving frame", we establish existence, uniqueness and stability results for mild and weak solutions of stochastic partial differential equations (SPDEs) with path dependent…

Probability · Mathematics 2010-01-18 Damir Filipovic , Stefan Tappe , Josef Teichmann

Strong Stability Preserving (SSP) time integration schemes maintain stability of the forward Euler method for any initial value problem. However, only a small subset of Runge-Kutta (RK) methods are SSP, and many efficient high-order time…

Numerical Analysis · Mathematics 2026-01-28 Mohammad R. Najafian , Brian C. Vermeire

In this paper, we propose a hybrid finite volume Hermite weighted essentially non-oscillatory (HWENO) scheme for solving one and two dimensional hyperbolic conservation laws. The zeroth-order and the first-order moments are used in the…

Numerical Analysis · Mathematics 2020-02-20 Zhuang Zhao , Yibing Chen , Jianxian Qiu

Current methods of evolving a spacetime containing one or more black holes are plagued by instabilities that prohibit long-term evolution. Some of these instabilities may be due to the numerical method used, traditionally finite…

General Relativity and Quantum Cosmology · Physics 2009-10-31 Lawrence E. Kidder , Mark A. Scheel , Saul A. Teukolsky , Eric D. Carlson , Gregory B. Cook

High-fidelity scientific simulations are now producing unprecedented amounts of data, creating a storage and analysis bottleneck. A single simulation can generate tremendous data volumes, often forcing researchers to discard valuable…

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