計算物理學導論(第2版) [An Introduction to Computational Physics(Second Edition)] pdf epub mobi txt 電子書 下載 2024
內容簡介
教育沒有什麼驚天動地的大事,隻要把每件小事做好,就能享受到教育的幸福。作者張曼淩老師就是這樣一位一直在享受教育幸福的教師。她結閤自己的經曆,從班級管理、課堂教學、個彆學生的教育、個性修煉及業餘生活等角度人手,告訴各位教師,隻要在細節上多用心,培養起學生學習及管理班級的積極性,不僅可以高效率地完成工作,還可以充分享受休閑生活。不把工作帶迴傢其實就是這麼簡單!
內頁插圖
精彩書評
小曼,一個必要的烏托邦。
那是一個以詩做底子的小曼,那是一個以善良做血肉的小曼,那是一個以夢想做心靈的小曼,更是一個以執著做意誌的 小曼。
我在貪婪地品鑒著小曼,品鑒著她的文字,是因為一貫生活在憤怒和絕望中的鄭傑,可以通過品鑒而知道除瞭批判,還有一種建設性的教育生活。
那麼多熱愛教育的人們都在品鑒她和她的文字,是為瞭印證人心裏期盼已久的安寜和渴望。
細細品鑒,浮現在你眼前的,是那肅殺氣氛裏難得的一縷幽香。
——中國知名校長、人們眼中的“另類校長” 鄭傑
讀小曼這本《魅力女教師修煉記》,我再次堅信:一個教師,是否“優秀”不是重要的,關鍵的是,是否“幸福”。因為“優秀”與否是彆人的評價,“幸福”與否是自己的感覺。小曼享受著學生,享受著工作,享受著每一個平凡而充實的日子,她因此而幸福。
——著名特級教師、成都武侯實驗中學校長 李鎮西
精緻,源於細緻;精緻,始於精心,成於精彩!小曼老師和她的教育生活,浪漫而精心,細膩又精緻,智慧且精彩!
——翔宇教育集團總校長、新教育研究院院長 戶誌文
小曼用心詮釋瞭她對生活、對教育的熱愛與敬畏。她的文字很快樂,很細膩,也很熱情,讀著讓人安靜,讓人溫暖。新教育讓小曼在平凡的工作中做齣瞭不平凡的成績,這不僅是一位年輕女教師自我修煉的提升,更反映瞭一個教育人的自省。
——吉林市勞動模範 陳久文
目錄
preface to first edition
preface
acknowledgments
1 introduction
1.1 computation and science
1.2 the emergence of modem computers
1.3 computer algorithms and languages
exercises
2 approximation of a function
2.1 interpolation
2.2 least-squares approximation
2.3 the millikan experiment
2.4 spline approximation
2.5 random-number generators
exercises
3 numerical calculus
3.1 numerical differentiation
3.2 numerical integration
3.3 roots of an equation
3.4 extremes of a function
3.5 classical scattering
exercises
4 ordinary differential equations
4.1 initial-value problems
4.2 the euler and picard methods
4.3 predictor-corrector methods
4.4 the runge-kutta method
4.5 chaotic dynamics of a driven pendulum
4.6 boundary-value and eigenvalue problems
4.7 the shooting method
4.8 linear equations and the sturm-liouville problem
4.9 the one-dimensional schr6dinger equation
exercises
5 numerical methods for matrices
5.1 matrices in physics
5.2 basic matrix operations
5.3 linear equation systems
5.4 zeros and extremes of multivariable functions
5.5 eigenvalue problems
5.6 the faddeev-leverrier method
5.7 complex zeros of a polynomial
5.8 electronic structures of atoms
5.9 the lanczos algorithm and the many-body problem
5.10 random matrices
exercises
6 spectral analysis
6.1 fourier analysis and orthogonal functions
6.2 discrete fourier transform
6.3 fast fourier transform
6.4 power spectrum of a driven pendulum
6.5 fourier transform in higher dimensions
6.6 wavelet analysis
6.7 discrete wavelet transform
6.8 special functions
6.9 gaussian quadratures
exercises
7 partial differential equations
7.1 partial differential equations in physics
7.2 separation of variables
7.3 discretization of the equation
7.4 the matrix method for difference equations
7.5 the relaxation method
7.6 groundwater dynamics
7.7 initial-value problems
7.8 temperature field of a nuclear waste rod
exercises
8 molecular dynamics simulations
8.1 general behavior of a classical system
8.2 basic methods for many-body systems
8.3 the verlet algorithm
8.4 structure of atomic clusters
8.5 the gear predictor-corrector method
8.6 constant pressure, temperature, and bond length
8.7 structure and dynamics of real materials
8.8 ab initio molecular dynamics
exercises
9 modeling continuous systems
9.1 hydrodynamic equations
9.2 the basic finite element method
9.3 the ritz variational method
9.4 higher-dimensional systems
9.5 the finite element method for nonlinear equations
9.6 the particle-in-cell method
9.7 hydrodynamics and magnetohydrodynamics
9.8 the lattice boltzmann method
exercises
10 monte carlo simulations
10.1 sampling and integration
10.2 the metropolis algorithm
10.3 applications in statistical physics
10.4 critical slowing down and block algorithms
10.5 variational quantum monte carlo simulations
10.6 green's function monte carlo simulations
10.7 two-dimensional electron gas
10.8 path-integral monte carlo simulations
10.9 quantum lattice models
exercises
11 genetic algorithm and programming
11.1 basic elements of a genetic algorithm
11.2 the thomson problem
11.3 continuous genetic algorithm
11.4 other applications
11.5 genetic programming
exercises
12 numerical renormalization
12.1 the scaling concept
12.2 renormalization transform
12.3 critical phenomena: the ising model
12.4 renormalization with monte carlo simulation
12.5 crossover: the kondo problem
12.6 quantum lattice renormalization
12.7 density matrix renormalization
exercises
references
index
精彩書摘
The basic idea behind a genetic algorithm is to follow the biological processof evolution in selecting the path to reach an optimal configuration of a givencomplex system. For exampie, for an interacting many-body system, the equilib-rium is reached by moving the system to the configuration that is at the globalminimum on its potential energy surface. This is single-objective optimization,which can be described mathematically as searching for the global minimum ofa multivariable function. Multiobjective optimization involvesmore than one equation, for example, a search for the minima of gk Both types ofoptimization can involve some constraints.We limit ourselves to single-objective optimization here. For a detailed dis-cussion on multi-objective optimization using the genetic algorithm, see Deb.
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前言/序言
計算物理學導論(第2版) [An Introduction to Computational Physics(Second Edition)] 下載 mobi epub pdf txt 電子書
計算物理學導論(第2版) [An Introduction to Computational Physics(Second Edition)] pdf epub mobi txt 電子書 下載