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GPT for Python-coding in computational materials science and mechanicsfrom prompt engineering to solutions in worked-out examples /

Record Type:	Electronic resources : Monograph/item
Title/Author:	GPT for Python-coding in computational materials science and mechanicsedited by Bernhard Eidel.
Reminder of title:	from prompt engineering to solutions in worked-out examples /
other author:	Eidel, Bernhard.
Published:	Cham :Springer Nature Switzerland :2025.
Description:	xii, 263 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Python (Computer program language)
Online resource:	https://doi.org/10.1007/978-3-031-85470-5
ISBN:	9783031854705$q(electronic bk.)

GPT for Python-coding in computational materials science and mechanicsfrom prompt engineering to solutions in worked-out examples /
GPT for Python-coding in computational materials science and mechanicsfrom prompt engineering to solutions in worked-out examples /[electronic resource] :edited by Bernhard Eidel. - Cham :Springer Nature Switzerland :2025. - xii, 263 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v. 11981860-9503 ;. - Studies in computational intelligence ;v. 216..

Topics of Computational Materials Science -- Generation of Atomic Scale Single Crystals -- Molecular Dynamics Simulation of Noble Gases -- Phase Field Modeling of Grain Growth.-Modeling Corrosion Using a Cellular Automaton -- Instationary Heat Conduction on Rectangular Domains with Arbitrary Circular Holes -- Topics of Deep Learning Based Materials Science -- Transfer Learning for Alloy Classification based on Microstructure Images -- Transfer Learning for Microstructure Image Segmentation -- Topics of Computational Analysis of Waves and Fluid Mechanics -- Elastic Wave Propagation -- Electromagnetic Wave Propagation in Dielectric Media -- Flow Around an Obstacle Using the Lattice Boltzmann Method.-Conclusions -- Learned Lessons - Recommendations.

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.

ISBN: 9783031854705$q(electronic bk.)

Standard No.: 10.1007/978-3-031-85470-5doiSubjects--Uniform Titles:

ChatGPT.
Subjects--Topical Terms:

215247
Python (Computer program language)

LC Class. No.: QA76.73.P98

Dewey Class. No.: 005.133

GPT for Python-coding in computational materials science and mechanicsfrom prompt engineering to solutions in worked-out examples /
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520 $a 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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