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1: 32.2 Differential Equations
§32.2(i) Introduction
The six Painlevé equations P I P VI  are as follows: … The solutions of P I P VI  are called the Painlevé transcendents. The six equations are sometimes referred to as the Painlevé transcendents, but in this chapter this term will be used only for their solutions. …
2: 18.42 Software
For another listing of Web-accessible software for the functions in this chapter, see GAMS (class C3). …
3: 32.16 Physical Applications
§32.16 Physical Applications
Statistical Physics
Integrable Continuous Dynamical Systems
Other Applications
For the Ising model see Barouch et al. (1973), Wu et al. (1976), and McCoy et al. (1977). …
4: 32 Painlevé Transcendents
Chapter 32 Painlevé Transcendents
5: 32.13 Reductions of Partial Differential Equations
§32.13 Reductions of Partial Differential Equations
§32.13(i) Korteweg–de Vries and Modified Korteweg–de Vries Equations
§32.13(ii) Sine-Gordon Equation
§32.13(iii) Boussinesq Equation
6: 32.12 Asymptotic Approximations for Complex Variables
§32.12 Asymptotic Approximations for Complex Variables
7: Bibliography I
  • K. Inkeri (1959) The real roots of Bernoulli polynomials. Ann. Univ. Turku. Ser. A I 37, pp. 1–20.
  • A. R. Its and A. A. Kapaev (1987) The method of isomonodromic deformations and relation formulas for the second Painlevé transcendent. Izv. Akad. Nauk SSSR Ser. Mat. 51 (4), pp. 878–892, 912 (Russian).
  • A. R. Its and A. A. Kapaev (2003) Quasi-linear Stokes phenomenon for the second Painlevé transcendent. Nonlinearity 16 (1), pp. 363–386.
  • A. R. Its and A. A. Kapaev (1998) Connection formulae for the fourth Painlevé transcendent; Clarkson-McLeod solution. J. Phys. A 31 (17), pp. 4073–4113.
  • 8: 20 Theta Functions
    Chapter 20 Theta Functions
    9: 32.17 Methods of Computation
    §32.17 Methods of Computation
    10: 32.14 Combinatorics
    §32.14 Combinatorics
    where the distribution function F ( s ) is defined here by … The distribution function F ( s ) given by (32.14.2) arises in random matrix theory where it gives the limiting distribution for the normalized largest eigenvalue in the Gaussian Unitary Ensemble of n × n Hermitian matrices; see Tracy and Widom (1994). …