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We present a framework for fine-tuning flow-matching generative models to enforce physical constraints and solve inverse problems in scientific systems. Starting from a model trained on low-fidelity or observational data, we apply a…

Machine Learning · Computer Science 2026-01-28 Jan Tauberschmidt , Sophie Fellenz , Sebastian J. Vollmer , Andrew B. Duncan

The paper compares computational aspects of four approaches to compute conservation laws of single differential equations (DEs) or systems of them, ODEs and PDEs. The only restriction, required by two of the four corresponding computer…

Symbolic Computation · Computer Science 2007-05-23 Thomas Wolf

In this work we study constant-coefficient first order systems of partial differential equations and give necessary and sufficient conditions for those systems to have a well posed Cauchy Problem. In many physical applications, due to the…

General Relativity and Quantum Cosmology · Physics 2021-11-17 Fernando Abalos , Oscar Reula

I present a new, simple method to dynamically control the growth of the discretized constraints during a free evolution of Einstein's equations. During an evolution, any given family of formulations is adjusted off the constraints surface…

General Relativity and Quantum Cosmology · Physics 2007-05-23 Manuel Tiglio

We investigate the regularity of local weak solutions to evolution equations of the form \[…

Analysis of PDEs · Mathematics 2026-04-23 Pasquale Ambrosio , Simone Ciani , Giovanni Cupini

The higher-order nonlinear Schrodinger equation (Dysthe's equation in the context of water-waves) models the time evolution of the slowly modulated amplitude of a wave-packet in dispersive partial differential equations (PDE). These…

Analysis of PDEs · Mathematics 2024-12-18 Jack Keeler , Alberto Alberello , Ben Humphries , Emilian Parau

In many experimental settings, one is tasked with obtaining information about certain relationships by applying perturbations to a set of independent variables and noting the changes in the set of dependent ones. While traditional…

Optimization and Control · Mathematics 2016-05-03 Gene A. Bunin

Computational electrodynamics (CED), the numerical solution of Maxwell's equations, plays an incredibly important role in several problems in science and engineering. High accuracy solutions are desired, and the discontinuous Galerkin (DG)…

Numerical Analysis · Mathematics 2019-06-26 Arijit Hazra , Praveen Chandrashekar , Dinshaw S. Balsara

Several important PDE systems, like magnetohydrodynamics and computational electrodynamics, are known to support involutions where the divergence of a vector field evolves in divergence-free or divergence constraint-preserving fashion.…

Numerical Analysis · Mathematics 2021-09-07 Dinshaw S. Balsara , Roger Käppeli , Walter Boscheri , Michael Dumbser

In this paper we prove necessary conditions for optimality of a stochastic control problem for a class of stochastic partial differential equations that is controlled through the boundary. This kind of problems can be interpreted as a…

Probability · Mathematics 2016-12-05 Giuseppina Guatteri

In this article, we investigate the existence and properties of time-periodic solutions for damped evolutionary partial differential equations subject to periodic forcing. Particular emphasis is placed on configurations where the energy…

Analysis of PDEs · Mathematics 2026-05-19 Camille Laurent , Ivonne Rivas

We study the following class of scalar hyperbolic conservation laws with discontinuous fluxes: \partial_t\rho+\partial_xF(x,\rho)=0. The main feature of such a conservation law is the discontinuity of the flux function in the space variable…

Analysis of PDEs · Mathematics 2007-10-02 Gui-Qiang Chen , Nadine Even , Christian Klingenberg

In this work, we develop a method for learning interpretable, thermodynamically stable and Galilean invariant partial differential equations (PDEs) based on the Conservation-dissipation Formalism of irreversible thermodynamics. As governing…

Computational Physics · Physics 2021-10-13 Juntao Huang , Zhiting Ma , Yizhou Zhou , Wen-An Yong

We study closed systems of particles that are subject to stochastic forces in addition to the conservative forces. The stochastic equations of motion are set up in such a way that the energy is strictly conserved at all times. To ensure…

Statistical Mechanics · Physics 2022-10-05 Tânia Tomé , Mário J. de Oliveira

In this paper, we present an abstract framework to obtain convergence rates for the approximation of random evolution equations corresponding to a random family of forms determined by finite-dimensional noise. The full discretization error…

Functional Analysis · Mathematics 2024-12-19 Katharina Klioba , Christian Seifert

We study the problem of stabilization for a class of evolution systems with fractional-damping. After writing the equations as an augmented system we prove in this article first that the problem is well posed. Second, using the LaSalle's…

Analysis of PDEs · Mathematics 2020-10-20 Kaïs Ammari , Fathi Hassine , Luc Robbiano

We prove existence and Sobolev regularity of solutions of a nonlinear system of degenerate-parabolic PDEs with self- and cross-diffusion, transport/confinement and nonlocal interaction terms. The macroscopic system of PDEs is formally…

Analysis of PDEs · Mathematics 2021-06-01 Luca Alasio , Maria Bruna , Simone Fagioli , Simon Schulz

In many scientific fields, the generation and evolution of data are governed by partial differential equations (PDEs) which are typically informed by established physical laws at the macroscopic level to describe general and predictable…

Methodology · Statistics 2025-07-01 Ziyuan Chen , Shunxing Yan , Fang Yao

We extend a contraction mapping argument for ordinary state-dependent delay differential equations to evolutionary partial differential equations in the sense of R. Picard, that is, to equations of the form $\bigl(\partial_{t}…

Analysis of PDEs · Mathematics 2025-11-20 Bernhard Aigner , Marcus Waurick

The working mechanisms of complex natural systems tend to abide by concise and profound partial differential equations (PDEs). Methods that directly mine equations from data are called PDE discovery, which reveals consistent physical laws…

Machine Learning · Computer Science 2023-03-17 Mengge Du , Yuntian Chen , Dongxiao Zhang