Feature Review Papers in AppliedMath

A special issue of AppliedMath (ISSN 2673-9909).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1535

Special Issue Editor


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Department of Pure and Applied Mathematics, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita 565-0871, Osaka, Japan
Interests: computational commutative algebra; discrete mathematics; combinatorics
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Special Issue Information

Dear Colleagues,

As the Editor-in-Chief of AppliedMath, I am pleased to announce the Special Issue "Feature Review Papers in AppliedMath". The field of applied mathematics plays a crucial role in addressing real-world problems across various disciplines, including engineering, physics, biology, medicine, finance, psychology, and data science. This Special Issue seeks to gather manuscripts that provide comprehensive reviews of significant advancements, methodologies, and applications in applied mathematics, showcasing both theoretical insights and practical implementations.

We invite contributions that cover, but are not limited to, the following topics:

Numerical methods.

Mathematical modeling.

Data analysis and statistical methods.

Computational mathematics.

Optimization techniques.

Control theory.

Integrated mathematical applications.

Prof. Dr. Takayuki Hibi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AppliedMath is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • numerical methods
  • mathematical modeling
  • data analysis and statistical methods
  • computational mathematics
  • optimization techniques
  • control theory
  • integrated mathematical applications

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Published Papers (1 paper)

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Review

36 pages, 5381 KB  
Review
Quantum-Inspired Neural Radiative Transfer (QINRT): A Multi-Scale Computational Framework for Next-Generation Climate Intelligence
by Muhammad Shoaib Akhtar
AppliedMath 2025, 5(4), 145; https://doi.org/10.3390/appliedmath5040145 - 23 Oct 2025
Viewed by 1002
Abstract
The increasing need for high-resolution, real-time radiative transfer (RT) modeling in climate science, remote sensing, and planetary exploration has exposed limitations of traditional solvers such as the Discrete Ordinate Radiative Transfer (DISORT) and Rapid Radiative Transfer Model for General Circulation Models (RRTMG), particularly [...] Read more.
The increasing need for high-resolution, real-time radiative transfer (RT) modeling in climate science, remote sensing, and planetary exploration has exposed limitations of traditional solvers such as the Discrete Ordinate Radiative Transfer (DISORT) and Rapid Radiative Transfer Model for General Circulation Models (RRTMG), particularly in handling spectral complexity, non-local thermodynamic equilibrium (non-LTE) conditions, and computational scalability. Quantum-Inspired Neural Radiative Transfer (QINRT) frameworks, combining tensor-network parameterizations and quantum neural operators (QNOs), offer efficient approximation of high-dimensional radiative fields while preserving key physical correlations. This review highlights the advances of QINRT in enhancing spectral fidelity and computational efficiency, enabling energy-efficient, real-time RT inference suitable for satellite constellations and unmanned aerial vehicle (UAV) platforms. By integrating physics-informed modeling with scalable neural architectures, QINRT represents a transformative approach for next-generation Earth-system digital twins and autonomous climate intelligence. Full article
(This article belongs to the Special Issue Feature Review Papers in AppliedMath)
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