BOOKS - TECHNICAL SCIENCES - Statistical Methods for Materials Science The Data Scien...
Statistical Methods for Materials Science The Data Science of Microstructure Characterization - Jeffrey P. Simmons, Lawrence F. Drummy, Charles A. Bouman, Marc De Graef 2019 PDF CRC Press BOOKS TECHNICAL SCIENCES
ECO~19 kg CO²

2 TON

Views
170915

Telegram
 
Statistical Methods for Materials Science The Data Science of Microstructure Characterization
Author: Jeffrey P. Simmons, Lawrence F. Drummy, Charles A. Bouman, Marc De Graef
Year: 2019
Pages: 537
Format: PDF
File size: 113,81 MB
Language: ENG



Book Description: Statistical Methods for Materials Science: The Data Science of Microstructure Characterization Author: Jeffrey P. Simmons, Lawrence F. Drummy, Charles A. Bouman, Marc De Graef 2019 Pages: 537 CRC Press Summary: In today's world, data analytics has become an integral part of materials science, and this book provides the practical tools and fundamentals needed for researchers in materials science to understand how to analyze large datasets using statistical methods, especially inverse methods applied to microstructure characterization. The book contains valuable guidance on essential topics such as denoising and data modeling, as well as analysis and applications sections that address compressed sensing methods, stochastic models, extreme estimation, and approaches to pattern detection. The Need to Study and Understand the Process of Technology Evolution: As technology continues to evolve at an unprecedented pace, it is crucial for us to study and understand the process of technological advancements to ensure the survival of humanity and the unity of people in a warring state. The development of modern knowledge is the basis for our survival, and it is essential to develop a personal paradigm for perceiving the technological process. This book serves as a comprehensive guide to understanding the statistical methods used in materials science, which is critical for the advancement of technology. The Need and Possibility of Developing a Personal Paradigm: To survive in a rapidly changing world, we must develop a personal paradigm for perceiving the technological process.
''
材料科学の統計的方法:微細構造のデータサイエンス特性著者: Jeffrey P。 mmons、 Lawrence F。 Drummy、 Charles A。 Bouman、 Marc De Graef 2019 Страницы: 537 Publisher: [Insert Publisher]まとめ今日の世界では、データ分析は材料科学の不可欠な部分となっており、この本は、材料科学の研究者が統計的方法、特に微細構造の特性化に適用される逆方式を使用して大きなデータセットを分析する方法を理解するために必要な実用的なツールと基礎を提供しています。この本には、ノイズ低減やデータモデリングなどの重要なトピックに関する貴重なガイダンスと、圧縮センシング技術、確率モデル、極端な評価、および画像検出アプローチに対処する分析および応用セクションが含まれています。技術の進化の過程を研究し、理解する必要性:技術が前例のないペースで発展し続けているので、私たちは人類の生存と戦争状態の人々の団結を確保するために技術の進歩のプロセスを研究し、理解することが重要です。現代の知識の発展は私たちの生存の基礎であり、技術プロセスの認識のための個人的なパラダイムを開発することが重要です。本書は、技術の発展に欠かせない材料科学における統計的手法を理解するための総合的なガイドとなっています。個人的なパラダイムを開発する必要性と可能性:急速に変化する世界で生き残るためには、技術プロセスの認識のための個人的なパラダイムを開発する必要があります。

You may also be interested in:

Statistical Methods for Materials Science The Data Science of Microstructure Characterization
Mechanical Vibration Methods for Studying Physical Properties of Solid Materials (Materials Science and Technologies)
Statistical Mechanics for Chemistry and Materials Science
Introduction to Statistical and Machine Learning Methods for Data Science
Statistical Methods An Introduction to Basic Statistical Concepts and Analysis, Second Edition
Chemistry, Physics, and Materials Science of Thermoelectric Materials: Beyond Bismuth Telluride (Fundamental Materials Research)
Statistical Theory: A Concise Introduction (Chapman and Hall CRC Texts in Statistical Science)
Statistical Machine Learning A Unified Framework (Chapman & Hall/CRC Texts in Statistical Science)
Statistical Analysis of Financial data With Examples In R (Chapman & Hall/CRC Texts in Statistical Science)
TMR Research in Insulating Granular Magnetic Materials (Materials Science and Technologies)
Advancements in Materials Science and Technology Led by Women (Advanced Structured Materials, 165)
Interactions Materials - Microorganisms: Concretes and Metals more Resistant to Biodeterioration (Science des materiaux Materials)
Composite Materials in Engineering Structures (Materials Science and Technologies)
Materials Science to Combat COVID-19 (Emerging Materials and Technologies)
Elementary Statistical Methods
The Science of Armour Materials (Woodhead Publishing in Materials)
Statistical Methods for Recommender Systems
Statistical Methods, Fourth Edition
Statistical Tableau How to Use Statistical Models and Decision Science in Tableau
Statistical Tableau How to Use Statistical Models and Decision Science in Tableau
Statistical Tableau: How to Use Statistical Models and Decision Science in Tableau
Statistical Tableau How to Use Statistical Models and Decision Science in Tableau
Dr. Laurie|s Introduction to Statistical Methods
Statistical Methods in Education and Psychology, Third Edition
Statistical Methods and Analyses for Medical Devices
Swarm Intelligence Methods for Statistical Regression
Hilbert Space Methods in Probability and Statistical Inference
Statistical and Econometric Methods for Transportation Data Analysis
Statistical Genomics (Methods in Molecular Biology, 2629)
Statistical Methods in the Atmospheric Sciences, Volume 100, Third Edition
Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS
Propensity Score Analysis Statistical Methods and Applications. Second Edition
Machine Learning for Materials Discovery: Numerical Recipes and Practical Applications (Machine Intelligence for Materials Science)
Applications Of Field Theory Methods In Statistical Physics Of Nonequilibrium Systems
Vibroacoustic Simulation An Introduction to Statistical Energy Analysis and Hybrid Methods
Methods in Statistical Mechanics: A Modern View (Lecture Notes in Physics)
Vibroacoustic Simulation: An Introduction to Statistical Energy Analysis and Hybrid Methods
System Reliability Theory Models, Statistical Methods, and Applications, Third Edition
Statistical Methods for Machine Learning Discover how to Transform Data into Knowledge with Python
Handbook of Material Science Research (Materials Science and Technologies)