Nirupam Chakraborti

Due to efficacy and optimization potential of genetic and evolutionary algorithms, they are used in learning and modeling especially with the advent of big data related problems. This book presents the algorithms and strategies specifically associated with pertinent issues in materials science domain. It discusses the procedures for evolutionary multi-objective optimization of objective functions created through these procedures and introduces available codes. Recent applications ranging from primary metal production to materials design are covered. It also describes hybrid modeling strategy, and other common modeling and simulation strategies like molecular dynamics, cellular automata etc. Features: Focuses on data-driven evolutionary modeling and optimization, including evolutionary deep learning. Include details on both algorithms and their applications in materials science and technology. Discusses hybrid data-driven modeling that couples evolutionary algorithms with generic computing strategies. Thoroughly discusses applications of pertinent strategies in metallurgy and materials. Provides overview of the major single and multi-objective evolutionary algorithms. This book aims at Researchers, Professionals, and Graduate students in Materials Science, Data-Driven Engineering, Metallurgical Engineering, Computational Materials Science, Structural Materials, and Functional Materials.

Due to efficacy and optimization potential of genetic and evolutionary algorithms, they are used in learning and modeling especially with the advent of big data related problems. This book presents the algorithms and strategies specifically associated with pertinent issues in materials science domain. It discusses the procedures for evolutionary multi-objective optimization of objective functions created through these procedures and introduces available codes. Recent applications ranging from primary metal production to materials design are covered. It also describes hybrid modeling strategy, and other common modeling and simulation strategies like molecular dynamics, cellular automata etc. Features: Focuses on data-driven evolutionary modeling and optimization, including evolutionary deep learning. Include details on both algorithms and their applications in materials science and technology. Discusses hybrid data-driven modeling that couples evolutionary algorithms with generic computing strategies. Thoroughly discusses applications of pertinent strategies in metallurgy and materials. Provides overview of the major single and multi-objective evolutionary algorithms. This book aims at Researchers, Professionals, and Graduate students in Materials Science, Data-Driven Engineering, Metallurgical Engineering, Computational Materials Science, Structural Materials, and Functional Materials.

On sangen erikoista, että intialainen professori tulee Suomeen ja ihastuu niin, että alkaa kirjoittaa runoja. Näin kävi Nirupam Chakrabortille, jonka alaa ei edes ole kirjallisuus vaan metallurgiateknologia! Niin syntyivät muiden muassa runot Helsinki, Turku, Jyväskylä, Lappeenranta... Onko kukaan ennen mahtanut sellaisia kirjoittaa? Runot on lisäksi kirjoitettu alkujaan bengalin kielellä, joka suomalaisen silmään näyttää koristeelliselta pitsiltä. Ne on julkaistu Intiassa runokokoelmassa Nijaswa Batas Boye Jai! (Sristisukh 2014). LUMOOJATAR - ENCHANTRESS on kaksikielinen kirja, jossa runot ovat sekä suomeksi että englanniksi. Yksi niistä on näytteeksi myös bengalinkielisenä. Chakraborti on itse laatinut runoistaan englanninkieliset versiot ja Hannele Pohjanmies on kääntänyt ne suomeksi. Kirja on pieni mutta sisältö on painavaa. Runoja on kourallinen, mutta ne ovat paljon puhuvia ja kertovat kirjoittajan ihailusta ja suurista tunteista Suomea kohtaan. Ne eivät kuitenkaan ole matkakertomuksia vaan kertovat myös muusta - niissä on omaperäistä syvällisyyttä, johon runoilija Suomen luonnossa on saanut kosketuksen!