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Beschreibung
Illustrating theoretical foundations and incorporating practitioners' first-hand experience, book is a practical guide to successful Autonomous Experimentation.
Illustrating theoretical foundations and incorporating practitioners' first-hand experience, book is a practical guide to successful Autonomous Experimentation.
Über den Autor

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.

Inhaltsverzeichnis

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

Details
Erscheinungsjahr: 2025
Fachbereich: Allgemeines
Genre: Importe, Wirtschaft
Rubrik: Recht & Wirtschaft
Medium: Taschenbuch
Inhalt: Einband - flex.(Paperback)
ISBN-13: 9781032417530
ISBN-10: 1032417536
Sprache: Englisch
Einband: Kartoniert / Broschiert
Redaktion: Noack, Marcus
Ushizima, Daniela
Hersteller: Chapman and Hall/CRC
Verantwortliche Person für die EU: Taylor & Francis Verlag GmbH, Kaufingerstr. 24, D-80331 München, gpsr@taylorandfrancis.com
Maße: 254 x 178 x 24 mm
Von/Mit: Marcus Noack (u. a.)
Erscheinungsdatum: 30.07.2025
Gewicht: 0,833 kg
Artikel-ID: 133626451