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Machine Learning for Metallic Corrosion Modeling: A Computational Exploration

(Autor)

Buch | Softcover
152 Seiten
2024
tredition (Verlag)
978-3-384-28034-3 (ISBN)
CHF 39,65 inkl. MwSt
Metal corrosion, from rusty cars to crumbling bridges, costs billions. Enter machine learning! This powerful tool analyzes vast amounts of data to predict and prevent corrosion. By simulating how metals interact with their environment, scientists can design better materials and protective coatings. It's a computational exploration to outsmart rust and save our infrastructure!

Professor Kiran is a leading expert in the field of olfaction, the sense of smell. With a distinguished career in sensory science, Professor Kiran's research has focused on unraveling the mysteries of the human nose and its profound impact on our lives. Professor Kiran's passion for the olfactory system stems from a deep fascination with its intricate workings and often underestimated influence. Their expertise encompasses the biological mechanisms of smell, the psychology of scent perception, and the far-reaching consequences of olfaction in health, behavior, and cognition. Professor Kiran's groundbreaking research has been published in top scientific journals and has garnered significant recognition within the scientific community. Their dedication to science communication extends beyond academia, as evidenced by their captivating book, "Smelling Sharp: How Your Nose Works and Why It Matters". In this book, Professor Kiran breaks down complex scientific concepts into an engaging and accessible narrative, inviting readers to explore the fascinating world of scent. Professor Kiran's expertise and captivating explanations make them a sought-after speaker and science communicator. Their tireless efforts to illuminate the power of smell promise to reshape our understanding of this remarkable sense.

Erscheint lt. Verlag 5.7.2024
Verlagsort mauli road
Sprache englisch
Maße 155 x 234 mm
Gewicht 266 g
Themenwelt Informatik Netzwerke Mail Server
Schlagworte algorithms • Computational • Data-driven • interface science • machine learning • metal alloys • Metallic Corrosion • Modeling • potential approach • Prediction • risk assessment
ISBN-10 3-384-28034-2 / 3384280342
ISBN-13 978-3-384-28034-3 / 9783384280343
Zustand Neuware
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