Differential Equations are somewhat pervasive in the description of natural phenomena and the theory of Ordinary Differential Equations is a basic framework where concepts, tools and results allow a systematic approach to knowledge. This same book aims to give a concrete proof of how the modeling of Nature is based on this theory and beyond. This appendix is intended to provide some concepts and results that are used in the text, referring to the student background and to textbooks for a full acquaintance of the material. We actually mention [ 2 , 3 , 5 , 7 , 10 ] as basic references on the subject.

数据流分析理论及实践Data Flow Analysis Theory and Practice

英国著名数学家哈代的一本经典的数论书。 第6版

A book with detail introduction to the LTE system.

这个是2020R1的理论指导书，英文版，自己觉得挺好的，希望能够帮助到学习ANSYS fluent的小伙们，加油加油。

Linear matrix inequalities in system and control theory

Daniel A. Spielman Dept. of Computer Science Program in Applied Mathematics Yale Unviersity

lurie的higher topos

neurkich代数数论

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.