Methods and Applications of Autonomous Experimentation
Chapman & Hall/CRC (Verlag)
978-1-032-31465-5 (ISBN)
Autonomous Experimentation is poised to revolutionize scientific experiments at advanced experimental facilities. 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 the need for human interference. Illustrating theoretical foundations and incorporating practitioners’ first-hand experiences, this 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.
Marcus M. Noack received his Ph.D. in applied mathematics from Oslo University, Norway. At Lawrence Berkeley National Laboratory, he is working on stochastic function approximation, optimization and uncertainty quantification, applied to Autonomous Experimentation. Daniela Ushizima, Ph.D. in physics from the University of Sao Paulo, Brazil after majoring in computer science, has been associated with Lawrence Berkeley National Laboratory since 2007, where she investigates machine learning algorithms applied to image processing. Her primary focus has been on developing computer vision software to automate scientific data analysis.
Preface
Contributors
Chapter 1 Autonomous Experimentation in Practice
Kevin G. Yager
Chapter 2 A Friendly Mathematical Perspective on Autonomous Experimentation
Marcus M. Noack
Chapter 3 A Perspective on Machine Learning for Autonomous Experimentation
Joshua Schrier and Alexander J. Norquist
Chapter 4 Gaussian Processes
Marcus M. Noack
Chapter 5 Uncertainty Quantification
Mark D. Risser and Marcus M. Noack
Chapter 6 Surrogate Model Guided Optimization
Juliane Mueller
Chapter 7 Artificial Neural Networks
Daniela Ushizima
Chapter 8 NSLS2
Philip M. Maffettone, Daniel B. Allan, Andi Barbour, Thomas A. Caswell, Dmitri Gavrilov, Marcus D. Handwell, Thomas Morris, Daniel Olds, Maksim Rakitin, Stuart I. Campbell and Bruce Ravel
Chapter 9 Reinforcement Learning
Yixuan Sun, Krishnan Raghavan and Prasanna Balaprakash
Chapter 10 Applications of Autonomous Methods to Synchrotron X-ray Scattering and Diffraction Experiments
Masafumi Fukuto, Yu-Chen Wiegart, Marcus M. Noack and Kevin G. Yager
Chapter 11 Autonomous Infrared Absorption Spectroscopy
Hoi-Ying Holman, Steven Lee, Liang Chen, Petrus H. Zwart and Marcus M. Noack
Chapter 12 Autonomous Hyperspectral Scanning Tunneling Spectroscopy
Antonio Rossi, Darian Smalley, Masahiro Ishigami, Eli Rotenberg, Alexander Weber-Barigoni and John C. Thomas
Chapter 13 Autonomous Control and Analyses of Fabricated Ecosystems
Trent R. Northern, Peter Andeer, Marcus M. Noack, Ptrus H. Zwart and Daniela Ushizima
Chapter 14 Autonomous Neutron Experiments
Martin Boehm, David E. Perryman, Alessio De Francesco, Luisa Scaccia, Alessandro Cunsolo, Tobias Weber, Yannick LeGoc and Paolo Mutti
Chapter 15 Material Discovery in Poorly Explored High-Dimensional Targeted Spaces
Suchismita Sarker and Apurva Mehta
Chapter 16 Autonomous Optical Microscopy for Exploring Nucleation and Growth of DNA Crystals
Aaron N. Michelson
Chapter 17 Constratined Autonomous Modelin of Metal-Mineral Adsorption
Elliot Chang, Linda Beverly and Haruko Wainwright
Chapter 18 Physics-In-The-Loop
Aaron Gilad Kusne
Chapter 19 A Closed Loop of Diverse Disciplines
Marucs M. Noack and Kevin G. Yager
Chapter 20 Analysis of Raw Data
Marcus M. Noack and Kevin G. Yager
Chapter 21 Autonomous Intelligent Decision Making
Marcus M. Noack and Kevin G. Yager
Chapter 22 Data Infrastructure
Marcus M. Noack and Kevin G. Yager
Bibliography
Index
| Erscheinungsdatum | 16.12.2023 |
|---|---|
| Reihe/Serie | Chapman & Hall/CRC Computational Science |
| Zusatzinfo | 5 Tables, black and white; 109 Line drawings, color; 8 Line drawings, black and white; 9 Halftones, color; 118 Illustrations, color; 8 Illustrations, black and white |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Gewicht | 980 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Software Entwicklung |
| Informatik ► Theorie / Studium ► Algorithmen | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-032-31465-6 / 1032314656 |
| ISBN-13 | 978-1-032-31465-5 / 9781032314655 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
