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Mathematics
Special Issues
Knowledge Discovery, Data Mining and Machine Learning
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Knowledge Discovery, Data Mining and Machine Learning

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 902

Special Issue Editors

Dr. Jing He

E-Mail Website
Guest Editor
Department of Neuroscience, University of Oxford, Oxford, UK
Interests: machine learning; artificial intelligence; additive manufactory; 3D printing; new drug design; graph isomorphism; linear programming; blockchain
Dr. Hui Zheng

E-Mail Website
Guest Editor
School of Software and Electrical Engineering, Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, Australia
Interests: data analytics; statistics; artificial intelligence; larguage modelling; uncertainty representation; muti-objective optimisation; causal relationship discovery; recommender system

Special Issue Information

Dear Colleagues,

The premier technical publication in the field, Machine Learning, Data Mining and Knowledge Discovery, is a resource that collects relevant common methods and techniques and is a forum for unifying the diverse constituent research communities.

This Special Issue focuses on the theory, techniques, and practice for extracting information from large databases. The journal publishes original technical papers in both the research and practice of data mining and knowledge discovery, surveys and tutorials of important areas and techniques, and detailed descriptions of significant applications.

Coverage includes:

  • Theory and foundational issues;
  • Data mining methods;
  • Algorithms for data mining;
  • Knowledge discovery process;
  • Application issues.

Dr. Jing He
Dr. Hui Zheng 
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • machine learning
  • artificial intelligence
  • knowledge discovery
  • new drug design
  • privacy preserving
  • additive manufactory

Published Papers (1 paper)

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Research

11 pages, 1796 KiB  
Article
Scalable Learning for Spatiotemporal Mean Field Games Using Physics-Informed Neural Operator
by Shuo Liu, Xu Chen and Xuan Di
Mathematics 2024, 12(6), 803; https://doi.org/10.3390/math12060803 - 08 Mar 2024
Viewed by 476
Abstract
This paper proposes a scalable learning framework to solve a system of coupled forward–backward partial differential equations (PDEs) arising from mean field games (MFGs). The MFG system incorporates a forward PDE to model the propagation of population dynamics and a backward PDE for [...] Read more.
This paper proposes a scalable learning framework to solve a system of coupled forward–backward partial differential equations (PDEs) arising from mean field games (MFGs). The MFG system incorporates a forward PDE to model the propagation of population dynamics and a backward PDE for a representative agent’s optimal control. Existing work mainly focus on solving the mean field game equilibrium (MFE) of the MFG system when given fixed boundary conditions, including the initial population state and terminal cost. To obtain MFE efficiently, particularly when the initial population density and terminal cost vary, we utilize a physics-informed neural operator (PINO) to tackle the forward–backward PDEs. A learning algorithm is devised and its performance is evaluated on one application domain, which is the autonomous driving velocity control. Numerical experiments show that our method can obtain the MFE accurately when given different initial distributions of vehicles. The PINO exhibits both memory efficiency and generalization capabilities compared to physics-informed neural networks (PINNs). Full article
(This article belongs to the Special Issue Knowledge Discovery, Data Mining and Machine Learning)
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