Isbn: 9786208441982 - ai driven material science for sustainable construction (10 risultati)

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Condizione: Neu. AI DRIVEN MATERIAL SCIENCE FOR SUSTAINABLE CONSTRUCTION | Vishnuvardhan S (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208441982 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. …

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Artificial Intelligence (AI) has revolutionized numerous fields, including material science, by enabling faster discovery, enhanced performance evaluation, and optimized material utilization. The construction industry, a critical sector responsible for global infrastructure, is experiencing a paradigm shift as AI-driven material science emerges as a cornerstone for sustainable development. The integration of AI in material science facilitates the design of innovative, eco-friendly materials that enhance durability, efficiency, and environmental sustainability.The traditional approach to material discovery often involves extensive trial-and-error experiments, which can be time-consuming and resource-intensive. AI, with its computational power and advanced algorithms, streamlines this process by predicting material properties, optimizing compositions, and identifying sustainable alternatives. Machine learning models, neural networks, and deep learning techniques enable researchers to analyze vast datasets, recognizing complex patterns that would be difficult to discern through conventional methods. 136 pp. Englisch.…

Condizione: New. Print on Demand.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Artificial Intelligence (AI) has revolutionized numerous fields, including material science, by enabling faster discovery, enhanced performance evaluation, and optimized material utilization. The construction industry, a critical sector responsible for global infrastructure, is experiencing a paradigm shift as AI-driven material science emerges as a cornerstone for sustainable development. The integration of AI in material science facilitates the design of innovative, eco-friendly materials that enhance durability, efficiency, and environmental sustainability.The traditional approach to material discovery often involves extensive trial-and-error experiments, which can be time-consuming and resource-intensive. AI, with its computational power and advanced algorithms, streamlines this process by predicting material properties, optimizing compositions, and identifying sustainable alternatives. Machine learning models, neural networks, and deep learning techniques enable researchers to analyze vast datasets, recognizing complex patterns that would be difficult to discern through conventional methods.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 136 pp. Englisch.…

Condizione: New. Print on Demand.

Condizione: New. PRINT ON DEMAND.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Artificial Intelligence (AI) has revolutionized numerous fields, including material science, by enabling faster discovery, enhanced performance evaluation, and optimized material utilization. The construction industry, a critical sector responsible for global infrastructure, is experiencing a paradigm shift as AI-driven material science emerges as a cornerstone for sustainable development. The integration of AI in material science facilitates the design of innovative, eco-friendly materials that enhance durability, efficiency, and environmental sustainability.The traditional approach to material discovery often involves extensive trial-and-error experiments, which can be time-consuming and resource-intensive. AI, with its computational power and advanced algorithms, streamlines this process by predicting material properties, optimizing compositions, and identifying sustainable alternatives. Machine learning models, neural networks, and deep learning techniques enable researchers to analyze vast datasets, recognizing complex patterns that would be difficult to discern through conventional methods.…