000			03358nas a22003255i 4500
001			3005-1436
003			DE-He213
005			20260610082539.0
007			cr nn 008mamaa
008			240321s||||||||sz uu poo|||||| b|EN |d
022			_a3005-1436
024	7		_a44379 _2local
210	1	0	_aMach. Learn. Comput. Sci. Eng
245	1	0	_aMachine Learning for Computational Science and Engineering _h[electronic resource] : _bModeling and Simulation / _cedited by Klaus-Jürgen Bathe, Timon Rabczuk.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer.
300			_bonline resource.
520			_aMachine 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	_aMathematics—Data processing.
650		0	_aMachine learning.
650		0	_aArtificial intelligence.
650	1	4	_aComputational Science and Engineering.
650	2	4	_aMachine Learning.
650	2	4	_aArtificial Intelligence.
700	1		_aBathe, Klaus-Jürgen. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aRabczuk, Timon. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
776	0	8	_iPrinted version: _x3005-1428
856	4	0	_uhttp://link.springer.com/journal/44379 _zHybrid
999			_c4825 _d4825