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Herausgeber: 
  • Bernhard Eidel
  • GPT for Python-Coding in Computational Materials Science and Mechanics: From Prompt Engineering to Solutions in Worked-Out Examples 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Juni 2025  
    Genre:  Naturwissensch., Medizin, Technik 
     
    Artificial Generative Intelligence / Artificial Intelligence / Automated Programming Assistant / ChatGPT / Computational Intelligence / Computational Materials Science / computational mechanics / Natural Language Processing
    ISBN:  9783031854699 
    EAN-Code: 
    9783031854699 
    Verlag:  Springer International Publishing 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Seiten:  263 
    Illustration:  XII, 263 p. 100 illus., 52 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    This book covers all the topics about ChatGPT required to successfully generate Python code to solve problems in computational materials science and mechanics, complemented by numerous fully worked-out applications. The complete work flow for AI-assisted coding is given, including: (i) prompt engineering providing a powerful toolset for how to give coding assignments to ChatGPT effectively; (ii) commented code listings; and (iii) tips and tricks to verify the codes in rigorous tests including human interventions to fix issues and gaps. Finally, (iv) the coding projects are critically reviewed to address the strengths and remaining weaknesses of the Chatbot, including explicit recommendations on how to communicate with GPT. For the steps (i)-(iv) the book presents a curated selection of intriguing problems from computational materials science and computational mechanics including machine learning for problem-solving. These problems are carefully chosen for their relevance to current research and industrial applications and their suitability for showcasing the advanced capabilities of GPT-4 for code generation. Spanning from predicting material behavior under various conditions to simulating complex mechanical interactions, the problems serve as a canvas on which GPT-4 paints its solutions, demonstrating not just accuracy but creativity in problem-solving. Therefore, the book serves as a valuable primer for both undergraduate and graduate students, as well as a review for research scientists and practicing engineers.

      



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