Results 21 to 30 of about 69,240 (308)

Homotopy-Sumudu transforms for solving system of fractional partial differential equations

open access: yesAdvances in Difference Equations, 2020
In this paper, we investigate the Sumudu transforms and homotopy analysis method (S-HAM) for solving a system of fractional partial differential equations. A general framework for solving such a kind of problems is presented.
A. K. Alomari
doaj   +1 more source

A New Modified Technique of Adomian Decomposition Method for Fractional Diffusion Equations with Initial-Boundary Conditions

open access: yesJournal of Function Spaces, 2022
In general, solving fractional partial differential equations either numerically or analytically is a difficult task. However, mathematicians have tried their best to make the task easy and promoted various techniques for their solutions. In this regard,
Saadia Masood   +8 more
doaj   +1 more source

Exact SMEFT formulation and expansion to O $$ \mathcal{O} $$ (v 4/Λ4)

open access: yesJournal of High Energy Physics, 2020
The Standard Model Effective Field Theory (SMEFT) theoretical framework is increasingly used to interpret particle physics measurements and constrain physics beyond the Standard Model.
Chris Hays   +3 more
doaj   +1 more source

Leading all-loop quantum contribution to the effective potential in general scalar field theory

open access: yesJournal of High Energy Physics, 2023
The RG equation for the effective potential in the leading log (LL) approximation is constructed which is valid for an arbitrary scalar field theory in 4 dimensions.
D. I. Kazakov   +2 more
doaj   +1 more source

Phylomitogenomics bolsters the high-level classification of Demospongiae (phylum Porifera).

open access: yesPLoS ONE, 2023
Class Demospongiae is the largest in the phylum Porifera (Sponges) and encompasses nearly 8,000 accepted species in three subclasses: Keratosa, Verongimorpha, and Heteroscleromorpha.
Dennis V Lavrov   +6 more
doaj   +1 more source

Generalized linear model for partially ordered data [PDF]

open access: yesStatistics in Medicine, 2011
Within the rich literature on generalized linear models, substantial efforts have been devoted to models for categorical responses that are either completely ordered or completely unordered. Few studies have focused on the analysis of partially ordered outcomes, which arise in practically every area of study, including medicine, the social sciences ...
Qiang, Zhang, Edward Haksing, Ip
openaire   +2 more sources

On some generalized inverses and partial orders in ∗-rings

open access: yesJournal of Algebra and Its Applications, 2022
Let [Formula: see text] be a unital ring with involution. The notions of 1MP-inverse and MP1-inverse are extended from [Formula: see text], the set of all [Formula: see text] matrices over [Formula: see text], to the set [Formula: see text] of all Moore–Penrose invertible elements in [Formula: see text].
Marovt, Janko   +2 more
openaire   +3 more sources

Universal lnfinite Partial Orders [PDF]

open access: yes, 1955
NOTE: Text or symbols not renderable in plain ASCII are indicated by [...]. Abstract is included in .pdf document. We consider infinite partial orders in which the order or comparability relations are transitive, non-reflexive, and nonsymmetric.
Johnston, John Beverley
core   +1 more source

Finite temperature and quench dynamics in the Transverse Field Ising Model from form factor expansions

open access: yesSciPost Physics, 2020
We consider the problems of calculating the dynamical order parameter two-point function at finite temperatures and the one-point function after a quantum quench in the transverse field Ising chain.
Etienne Granet, Maurizio Fagotti, Fabian H. L. Essler
doaj   +1 more source

Affine transformations accelerate the training of physics-informed neural networks of a one-dimensional consolidation problem

open access: yesScientific Reports, 2023
Physics-informed neural networks (PINNs) leverage data and knowledge about a problem. They provide a nonnumerical pathway to solving partial differential equations by expressing the field solution as an artificial neural network.
Luis Mandl   +3 more
doaj   +1 more source

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