Neural Networks and Modelling Applied in Engineering
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Network Science".
Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 460
Special Issue Editors
Interests: machine learning; mathematic modelling; design of experiment; prediction; mechanical engineering
2. Institute of Technology and Business in České Budějovice, Okružní 10, 370 01 České Budějovice, Czech Republic
Interests: modelling; materials; surface engineering; mechanical parameters; neural networks
Special Issues, Collections and Topics in MDPI journals
2. Institute of Technology and Business in České Budějovice, Okružní 10, 370 01 České Budějovice, Czech Republic
Interests: advanced materials; mechanical properties; surface engineering; coatings; applied physics; modelling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Every breakthrough idea, innovative method, and even completely ordinary experiment only becomes truly valuable when put to use in real-world situations. Artificial intelligence is becoming increasingly prevalent in information technology, but it still has a large amount of potential to be used to solve engineering challenges. A significant impulse for resolving difficult practical challenges is frequently provided by the interaction of abstract modelling with actual engineering knowledge. In engineering and industry, the use of expensive experiments or physical models frequently fails to produce results and solutions in line with the resources invested. Implementing neural networks or other mathematical models based on a planned experiment frequently results in interpretive issues but primarily offers practical solutions. In this Special Issue, we welcome submissions on the application of neural networks and mathematical modeling in engineering. Submissions must meet the requirements of uniqueness, originality, and scientific achievements. Papers that present methods to address particular problems that occur when applying neural networks and mathematical models to real-world issues in all areas of engineering (civil, mechanical, electrical, chemical, etc.), as well as papers that present original concepts, research results, and unconventional or previously unpublished applications of these techniques, are also encouraged.
Dr. Milan Kadnár
Dr. Marta Harničárová
Dr. Jan Valíček
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
- artificial neural networks
- neurons
- nonlinear systems
- network topology
- learning algorithm
- mathematical models
- design of experiment
- statistical methods
- machine learning
- prediction
- optimization
- deep neural networks
- engineering application
- mathematics
- artificial intelligence
