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Data Analysis Techniques for Physical Scientists
A comprehensive guide to data analysis techniques for the physical sciences including probability, statistics, data reconstruction, data correction and Monte Carlo methods. This book provides a valuable resource for advanced undergraduate and graduate students, as well as practitioners in the fields of experimental particle physics, nuclear physics and astrophysics.
Data Analysis for Physical Scientists
Introducing data analysis techniques to help undergraduate students develop the tools necessary for studying and working in the physical sciences.
Deep Learning for Physical Scientists
Discover the power of machine learning in the physical sciences with this one-stop resource from a leading voice in the field Deep Learning for Physical Scientists: Accelerating Research with Machine Learning delivers an insightful analysis of the transformative techniques being used in deep learning within the physical sciences. The book offers readers the ability to understand, select, and apply the best deep learning techniques for their individual research problem and interpret the outcome. Designed to teach researchers to think in useful new ways about how to achieve results in their research, the book provides scientists with new avenues to attack problems and avoid common pitfalls and...
Physics Problems for Aspiring Physical Scientists and Engineers
Containing over 200 physics problems, with hints and full solutions, this book develops the skill of finding solutions to scientific problems.
Data Analysis with Excel®
An essential introduction to data analysis techniques using spreadsheets, for undergraduate and graduate students.
Introductory Bioelectronics
Bioelectronics is a rich field of research involving the application of electronics engineering principles to biology, medicine, and the health sciences. With its interdisciplinary nature, bioelectronics spans state-of-the-art research at the interface between the life sciences, engineering and physical sciences. Introductory Bioelectronics offers a concise overview of the field and teaches the fundamentals of biochemical, biophysical, electrical, and physiological concepts relevant to bioelectronics. It is the first book to bring together these various topics, and to explain the basic theory and practical applications at an introductory level. The authors describe and contextualise the scie...
Uncertainty Quantification and Predictive Computational Science
This textbook teaches the essential background and skills for understanding and quantifying uncertainties in a computational simulation, and for predicting the behavior of a system under those uncertainties. It addresses a critical knowledge gap in the widespread adoption of simulation in high-consequence decision-making throughout the engineering and physical sciences. Constructing sophisticated techniques for prediction from basic building blocks, the book first reviews the fundamentals that underpin later topics of the book including probability, sampling, and Bayesian statistics. Part II focuses on applying Local Sensitivity Analysis to apportion uncertainty in the model outputs to sourc...
Deep Learning for Physical Scientists
Discover the power of machine learning in the physical sciences with this one-stop resource from a leading voice in the field Deep Learning for Physical Scientists: Accelerating Research with Machine Learning delivers an insightful analysis of the transformative techniques being used in deep learning within the physical sciences. The book offers readers the ability to understand, select, and apply the best deep learning techniques for their individual research problem and interpret the outcome. Designed to teach researchers to think in useful new ways about how to achieve results in their research, the book provides scientists with new avenues to attack problems and avoid common pitfalls and...
Applied Biophysics
This book presents the fundamentals of molecular biophysics, and highlights the connection between molecules and biological phenomena, making it an important text across a variety of science disciplines. The topics covered in the book include: Phase transitions that occur in biosystems (protein crystallisation, globule-coil transition etc) Liquid crystallinity as an example of the delicate range of partially ordered phases found with biological molecules How molecules move and propel themselves at the cellular level The general features of self-assembly with examples from proteins The phase behaviour of DNA The physical toolbox presented within this text will form a basis for students to enter into a wide range of pure and applied bioengineering fields in medical, food and pharmaceutical areas.
Markov Processes
Markov process theory provides a mathematical framework for analyzing the elements of randomness that are involved in most real-world dynamical processes. This introductory text, which requires an understanding of ordinary calculus, develops the concepts and results of random variable theory.
