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Difference equations have many applications and play an important role in numerical analysis, probability, statistics, combinatorics, computer science, quantum consciousness, etc. We first prove that the partial differential equation is…

Analysis of PDEs · Mathematics 2025-02-24 Chun-Kai Hwang , Tzon-Tzer Lu

We first highlight the main differences between second order and higher order linear parabolic equations. Then we survey existing results for the latter, in particular by analyzing the behavior of the convolution kernels. We illustrate the…

Analysis of PDEs · Mathematics 2012-12-21 Gerassimos Barbatis , Filippo Gazzola

In the context of modeling of cell signaling pathways, a relevant step is finding steady-state solutions for ODE systems that describe the kinetics of a set of chemical reactions, especially sets composed of zero, first, and second-order…

Computational Complexity · Computer Science 2018-04-11 Marcelo S. Reis

To approximate solutions of complex nonlinear partial differential equations remains a computational challenge, especially for sets of equations relevant in industry, such as Euler or Navier-Stokes equations. Even the most sophisticated…

Quantum Physics · Physics 2026-03-25 Maximilian Mandelt Buxadé , Stefan Langer , Philipp Bekemeyer

We present a framework for constructing a first-order hyperbolic system whose solution approximates that of a desired higher-order evolution equation. Constructions of this kind have received increasing interest in recent years, and are…

Analysis of PDEs · Mathematics 2025-05-19 David I. Ketcheson , Abhijit Biswas

The theory of QED corrections to hyperfine structure in light hydrogenic atoms and ions has recently advanced to the point that the uncertainty of these corrections is much smaller than 1 part per million (ppm), while the experiments are…

Nuclear Theory · Physics 2014-11-18 J. L. Friar , G. L. Payne

The hybrid neural differentiable models mark a significant advancement in the field of scientific machine learning. These models, integrating numerical representations of known physics into deep neural networks, offer enhanced predictive…

Machine Learning · Computer Science 2024-01-02 Deepak Akhare , Tengfei Luo , Jian-Xun Wang

We propose a novel hybrid high-order method (HHO) to approximate singularly perturbed fourth-order PDEs on domains with a possibly curved boundary. The two key ideas in devising the method are the use of a Nitsche-type boundary penalty…

Numerical Analysis · Mathematics 2021-12-07 Zhaonan Dong , Alexandre Ern

This work investigates the application of the Newton's method for the numerical solution of a nonlinear boundary value problem formulated through an ordinary differential equation (ODE). Nonlinear ODEs arise in various mathematical modeling…

We develop a higher-order perturbation theory for large-scale structure formation involving a free-streaming hot or warm dark matter species. We focus on the case of mixed cold dark matter and massive neutrinos, although our approach is…

Cosmology and Nongalactic Astrophysics · Physics 2016-08-08 Florian Führer , Yvonne Y. Y. Wong

One of the challenges that must be overcome in order to determine the neutrino mass hierarchy using reactor neutrinos is the theoretical uncertainty in the unoscillated reactor neutrino spectrum: this is one of the reasons why, recently, it…

High Energy Physics - Phenomenology · Physics 2020-01-08 Emilio Ciuffoli , Jarah Evslin , Hosam Mohammed

Equations, particularly differential equations, are fundamental for understanding natural phenomena and predicting complex dynamics across various scientific and engineering disciplines. However, the governing equations for many complex…

Machine Learning · Computer Science 2025-04-15 Junfeng Chen , Kailiang Wu , Dongbin Xiu

We present a new method for resolving combinatorial ambiguities that arise in multi-particle decay chains at hadron colliders where the assignment of visible particles to the different decay chains has ambiguities. Our method, based on…

High Energy Physics - Phenomenology · Physics 2011-05-25 Arvind Rajaraman , Felix Yu

The method of this paper is my original creation. A new method for solving linear differential equations is proposed in this paper. The important conclusion of this paper is that arbitrary order linear ordinary differential equations with…

General Mathematics · Mathematics 2018-06-05 Daiyuan Zhang

We present a method for computing uncertainties in spectral models, i.e. level populations, line emissivities, and emission line ratios, based upon the propagation of uncertainties originating from atomic data. We provide analytic…

Instrumentation and Methods for Astrophysics · Physics 2015-06-12 Manuel A. Bautista , Vanessa Fivet , Pascal Quinet , Jay Dunn , Theodore R. Gull. Timothy R. Kallman , Claudio Mendoza

In this paper I discuss how to consistently incorporate higher-order corrections to the bubble-nucleation rate at finite temperature. Doing so I examine the merits of different approaches, with the goal of reducing uncertainties for…

High Energy Physics - Phenomenology · Physics 2022-03-01 Andreas Ekstedt

Differential equations arise in mathematics, physics,medicine, pharmacology, communications, image processing and animation, etc. An Ordinary Differential Equation (ODE) is a differential equation if it involves derivatives with respect to…

Mathematical Software · Computer Science 2015-12-31 A. O. Anidu , S. A. Arekete , A. O. Adedayo , A. O. Adekoya

New problem is considered that is to find nonlinear differential equations with special solutions. Method is presented to construct nonlinear ordinary differential equations with exact solution. Crucial step to the method is the assumption…

Exactly Solvable and Integrable Systems · Physics 2007-05-23 N. A. Kudryashov

This chapter provides an introduction to Hybrid High-Order (HHO) methods. These are new generation numerical methods for PDEs with several advantageous features: the support of arbitrary approximation orders on general polyhedral meshes,…

Numerical Analysis · Mathematics 2017-04-21 Daniele A. Di Pietro , Roberta Tittarelli

While quantum computing provides an exponential advantage in solving linear differential equations, there are relatively few quantum algorithms for solving nonlinear differential equations. In our work, based on the homotopy perturbation…

Quantum Physics · Physics 2021-12-23 Cheng Xue , Yu-Chun Wu , Guo-Ping Guo