On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory - Fabian Guignard
-40% ar kodu BOOKS
Piegāde 12-18 darba dienu laikā
30 dienu atgriešanas politika
The gathering and storage of data indexed in space and time are experiencing unprecedented growth, demanding for advanced and adapted tools to analyse them. This thesis deals with the exploration and modelling of complex high-frequency and non-stationary spatio-temporal data. It proposes an efficient framework in modelling with machine learning algorithms spatio-temporal fields measured on irregular monitor ... Pilns apraksts
Jums varētu patikt arī
TOP
INCOSE Systems Engineering Handbook
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
If Anyone Builds It, Everyone Dies: The Case Against Superintelligent AI
Eliezer Yudkowsky, Nate Soares
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Open Circuits: The Inner Beauty of Electronic Components
Eric Schlaepfer, Windell Oskay
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Get started with MicroPython on Raspberry Pi Pico: The Official Raspberry Pi Pico Guide
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
We Are Not Machines: The Fight for the Future of Work
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Foundations of Flavor: The Noma Guide to Fermentation: Including Step-By-Step Information on Making and Cooking with: Koji, Kombuchas, Shoyus, Misos, Vinegars, Garums, Lacto-Ferments and black Fruits Vegetables
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Nucleo Boards Programming with the STM32CubeIDE: Hands-on in more than 50 projects
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
Steven L. Brunton, J. Nathan Kutz
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Fundamentals of Aerodynamics ISE
Christopher Cadou, John D. Anderson
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Black Holes: The Key to Understanding the Universe
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Practical Electronics for Inventors
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Flowers For Algernon: A Modern Literary Classic
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Human Compatible: Artificial Intelligence and the Problem of Control
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
How To Read Water: Clues & Patterns from Puddles to the Sea
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Make: Electronics: Learning by Discovery: A hands-on primer for the new electronics enthusiast
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
Mastering the Arduino Uno R4: Programming and Projects for the Minima and WiFi
-40% ar kodu BOOKS
Piegādātāja noliktavā
TOP
The Idea Factory: Bell Labs and the Great Age of American Innovation
-40% ar kodu BOOKS
Piegādātāja noliktavā
Aprašymas
The gathering and storage of data indexed in space and time are experiencing unprecedented growth, demanding for advanced and adapted tools to analyse them. This thesis deals with the exploration and modelling of complex high-frequency and non-stationary spatio-temporal data. It proposes an efficient framework in modelling with machine learning algorithms spatio-temporal fields measured on irregular monitoring networks, accounting for high dimensional input space and large data sets. The uncertainty quantification is enabled by specifying this framework with the extreme learning machine, a particular type of artificial neural network for which analytical results, variance estimation and confidence intervals are developed. Particular attention is also paid to a highly versatile exploratory data analysis tool based on information theory, the Fisher-Shannon analysis, which can be used to assess the complexity of distributional properties of temporal, spatial and spatio-temporal data sets. Examples of the proposed methodologies are concentrated on data from environmental sciences, with an emphasis on wind speed modelling in complex mountainous terrain and the resulting renewable energy assessment. The contributions of this thesis can find a large number of applications in several research domains where exploration, understanding, clustering, interpolation and forecasting of complex phenomena are of utmost importance.
Vairāk informācijas
| Autors | Fabian Guignard |
|---|---|
| Izdevējs | Springer Nature Switzerland |
| Series | Springer Theses |
| Izlaides gads | 2023 |
| Vāka tips | Mīkstais vāks |
| EAN | 9783030952334 |