01595nam 2200457 450 991027104360332120180629083627.01-118-91466-X1-5231-1476-21-118-91465-11-118-91467-8(CKB)4330000000007474(MiAaPQ)EBC4838301(EXLCZ)99433000000000747420170426h20172017 uy 0engurcnu||||||||rdacontentrdamediardacarrierProcess modeling and simulation for chemical engineers theory and practice /Simant Ranjan UpretiChichester, West Sussex, England :Wiley,2017.©20171 online resource (364 pages)1-118-91468-6 Includes bibliographical references and index.Fundamental relations -- Constitutive relations -- Model formulation -- Model transformation -- Model simplification and approximation -- Process simulation -- Mathematical review.Chemical processesMathematical modelsChemical processesData processingElectronic books.Chemical processesMathematical models.Chemical processesData processing.660/.284401Upreti Simant Ranjan965283MiAaPQMiAaPQMiAaPQBOOK9910271043603321Process modeling and simulation for chemical engineers2800822UNINA00980nam a2200265 i 4500991002530759707536140528s2013 us b 001 0 eng d9780253001825b1418980x-39ule_instDip. di Studi Umanisticiita184Plato's laws :force and truth in politics /edited by Gregory Recco and Eric SandayBloomington, Ind. :Indiana University Press,c2013248 p. ;24 cmStudies in continental thoughtContiene riferimenti bibliografici. IndicePlatone.LegesRecco, GregorySanday, Eric.b1418980x31-07-1428-05-14991002530759707536LE007 880.1 Plato REC 01.50112007000253717le007pE20.97-l- 00000.i1562890531-07-14Plato's laws259374UNISALENTOle00728-05-14ma -engus 0003092nam 2200481z- 450 991105303730332120230911(CKB)5690000000228525(oapen)doab113956(EXLCZ)99569000000022852520230920c2023uuuu -u- -engurmn|---annantxtrdacontentcrdamediacrrdacarrierAdvances in Machine Learning and Mathematical Modeling for Optimization ProblemsMDPI - Multidisciplinary Digital Publishing Institute20231 online resource (280 p.)3-0365-7741-6 Machine learning and deep learning have made tremendous progress over the last decade and have become the de facto standard across a wide range of image, video, text, and sound processing domains, from object recognition to image generation. Recently, deep learning and deep reinforcement learning have begun to develop end-to-end training to solve more complex operation research and combinatorial optimization problems, such as covering problems, vehicle routing problems, traveling salesman problems, scheduling problems, and other complex problems requiring general simulations. These methods also sometimes include classic search and optimization algorithms for machine learning, such as Monte Carlo Tree Search in AlphaGO. The present reprint contains all of the articles accepted and published in the Special Issue of Mathematics entitled "Advances in Machine Learning and Mathematical Modeling for Optimization Problems". The articles presented in this Special Issue provide insights into related fields, including models, performance evaluation and improvements, and application developments. We hope that readers will benefit from the insights of these papers and contribute to these rapidly growing areas. We also hope that this Special Issue will shed light on major developments in the area of machine learning and mathematical modeling for optimization problems and that it will attract the attention of the scientific community to pursue further investigations, leading to the rapid implementation of these techniques.Mathematics & sciencebicsscResearch & information: generalbicsscartificial neural networks (ANNs)convex minimization problemsdecision theorydeep reinforcement learningend-to-end learningevolutionary computationfeature selectionmachine learningoptimization problemspickup and deliveryresource allocationstatistical learningtraveling salesman problemvehicle routing problemMathematics & scienceResearch & information: generalBOOK9911053037303321Advances in Machine Learning and Mathematical Modeling for Optimization Problems4525094UNINA