Machine Learning and Statistical Learning with Applications

Dear Colleagues, 

With the rapid advancement of artificial intelligence, machine learning, and statistical learning, high-dimensionality, big data, data imbalance, and out-of-distribution data have posed significant challenges for academic and industrial applications. Artificial intelligence models based on machine learning (ML) and statistical learning (SL) are employed in analyzing data. ML methods play significant roles in many research directions. Various machine learning technologies have been developed in diverse application domains. Such technology has solved numerous complex engineering and science problems. Machine learning is one of the fastest-growing active research areas. The Special Issue aims to have a collection of recent advances in machine learning. This Special Issue on "Machine Learning and Statistical Learning with Applications" will focus on publishing high-quality original research studies that address challenges in machine learning and statistical learning and their applications in science and engineering. Topics include but are not limited to the following:

• ML and SL model algorithm developments;
• ML and SL applications for predictive science and engineering;
• Physics-informed neural network model development and applications;
• Operator learning model development and applications;
• ML algorithms and approaches to handling out-of-distribution, data imbalance,  data fusion, etc.;
• Federated learning algorithm development and applications;
• Differential privacy-based ML algorithm development and applications;
• Uncertainty quantification for ML and SL algorithms and applications;
• Large-language model development and applications.

Prof. Dr. Guang Lin
Dr. Zecheng Zhang
Dr. Christian Moya
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.

• ML and SL model algorithm developments
• ML and SL applications for predictive science and engineering
• physics-informed neural network model development and applications
• operator learning model development and applications
• ML algorithms and approaches to handling out-of-distribution, data imbalance, data fusion, etc.
• federated learning algorithm development and applications
• differential privacy-based ML algorithm development and applications
• uncertainty quantification for ML and SL algorithms and applications
• large-language model development and applications