Machine Learning for Computational Science and Engineering (Record no. 4825)
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| fixed length control field | 03358nas a22003255i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 3005-1436 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260610082539.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240321s||||||||sz uu poo|||||| b|EN |d |
| 022 ## - INTERNATIONAL STANDARD SERIAL NUMBER | |
| International Standard Serial Number | 3005-1436 |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 44379 |
| Source of number or code | local |
| 210 10 - ABBREVIATED TITLE | |
| Abbreviated title | Mach. Learn. Comput. Sci. Eng |
| 245 10 - TITLE STATEMENT | |
| Title | Machine Learning for Computational Science and Engineering |
| Medium | [electronic resource] : |
| Remainder of title | Modeling and Simulation / |
| Statement of responsibility, etc. | edited by Klaus-Jürgen Bathe, Timon Rabczuk. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Cham : |
| Name of producer, publisher, distributor, manufacturer | Springer International Publishing : |
| -- | Imprint: Springer. |
| 300 ## - online resource | |
| Other physical details | online resource. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Machine learning (ML) approaches have been extensively and successfully employed in various areas, like in economics, medical predictions, face recognition, credit card fraud detection, and spam filtering. There is clearly also the potential that ML techniques developed in Engineering and the Sciences will drastically increase the possibilities of computational procedures, analysis and accelerate the design to analysis time. With the use of ML techniques, coupled to conventional methods like finite element and digital twin technologies, new avenues of computational procedures, modeling and simulation can be opened but the potential of these ML techniques needs to still be fully harvested, that is, specific methods need to be developed and enhanced. Then the use of classical analysis procedures driven by novel physics-based ML techniques promises to reach much more complex system analyses than so far possible, for example the long-time health-monitoring of complex structures, in the broadest sense. The objective of this journal is to publish papers of ML in Engineering and the Sciences presenting fundamental developments in computational procedures with a focus on the next generation of computer modeling and simulation in Engineering and the Sciences. The use of pure ML methods, the development of physics-based ML methods, and the coupling of ML with traditional analysis techniques, like finite element procedures, is of interest. The contributions can pertain to developments in all areas of Engineering and the Sciences. Emphasis in the papers should be on advancing the fields of computational procedures for modeling and simulation through the use of machine learning. The focus of ML in Machine Learning for Computational Science and Engineering is on having an impact on the practice of simulations in Engineering and the Sciences as found in many industries and research endeavours, such as civil and environmental, infrastructure, earthquake, mechanical, robotics, biomechanical, automotive, aeronautical, and ocean engineering, and including the various fields of sciences, where ML can be of use. . |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Mathematics—Data processing. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Artificial intelligence. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Computational Science and Engineering. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine Learning. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Artificial Intelligence. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Bathe, Klaus-Jürgen. |
| Relator term | editor. |
| Relationship | edt |
| -- | http://id.loc.gov/vocabulary/relators/edt |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Rabczuk, Timon. |
| Relator term | editor. |
| Relationship | edt |
| -- | http://id.loc.gov/vocabulary/relators/edt |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | SpringerLink (Online service) |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Printed version: |
| International Standard Serial Number | 3005-1428 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://link.springer.com/journal/44379">http://link.springer.com/journal/44379</a> |
| Public note | Hybrid |
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