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Methods and Applications of Autonomous Experimentation: Daniela Ushizima, Marcus Noack Methods and Applications of Autonomous Experimentation
Daniela Ushizima, Marcus Noack
R2,367 Discovery Miles 23 670 Ships in 10 - 15 working days

Autonomous Experimentation is poised to revolutionize scientific experiments at advanced facilities worldwide. Whereas previously, human experimenters were burdened with the laborious task of overseeing each measurement, recent advances in mathematics, machine learning and algorithms have alleviated this burden by enabling automated and intelligent decision-making, minimizing human interference. Illustrating theoretical foundations and incorporating practitioners’ first-hand experience, book is a practical guide to successful Autonomous Experimentation. Despite the field’s growing potential, there exists numerous myths and misconceptions surrounding Autonomous Experimentation. Combining insights from theorists, machine-learning engineers and applied scientists, this book aims to lay the foundation for future research and widespread adoption within the scientific community. This book is particularly useful for members of the scientific community looking to improve their research methods, but also contains additional insights for students and industry professionals interested in the future of the field.

Methods and Applications of Autonomous Experimentation: Daniela Ushizima, Marcus Noack Methods and Applications of Autonomous Experimentation
Daniela Ushizima, Marcus Noack
R1,633 Discovery Miles 16 330 Ships in 10 - 15 working days

Autonomous Experimentation is poised to revolutionize scientific experiments at advanced facilities worldwide. Whereas previously, human experimenters were burdened with the laborious task of overseeing each measurement, recent advances in mathematics, machine learning and algorithms have alleviated this burden by enabling automated and intelligent decision-making, minimizing human interference. Illustrating theoretical foundations and incorporating practitioners’ first-hand experience, book is a practical guide to successful Autonomous Experimentation. Despite the field’s growing potential, there exists numerous myths and misconceptions surrounding Autonomous Experimentation. Combining insights from theorists, machine-learning engineers and applied scientists, this book aims to lay the foundation for future research and widespread adoption within the scientific community. This book is particularly useful for members of the scientific community looking to improve their research methods, but also contains additional insights for students and industry professionals interested in the future of the field.

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