User:TMM53/Inverse scattering transform-2024-03-27
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In mathematics, the inverse scattering transform is a method that solves the initial value problem for a nonlinear partial differential equation using mathematical methods related to wave scattering.[1]: 4960 The direct scattering transform describes how a function scatters waves or generates bound-states.[2]: 39–43 The inverse scattering transform uses wave scattering data to construct the function responsible for wave scattering.[2]: 66–67 The direct and inverse scattering transforms are analogous to the direct and inverse Fourier transforms which are used to solve linear partial differential equations.[2]: 66–67
Using a pair of differential operators, a 3-step algorithm may solve nonlinear differential equations; the initial solution is transformed to scattering data (direct scattering transform), the scattering data evolves forward in time (time evolution), and the scattering data reconstructs the solution forward in time (inverse scattering transform).[2]: 66–67
This algorithm simplifies solving a nonlinear partial differential equation to solving 2 linear ordinary differential equations and an ordinary integral equation, a method ultimately leading to analytic solutions for many otherwise difficult to solve nonlinear partial differential equations.[2]: 72
History
[edit]The inverse scattering transform arose from studying solitary waves. J.S. Russell described a "wave of translation" or "solitary wave" occurring in shallow water.[3] First J.V. Boussinesq and later D. Korteweg and G. deVries discovered the Korteweg-deVries (KdV) equation, a nonlinear partial differential equation describing these waves.[3] Later, N. Zabusky and M. Kruskal, using numerical methods for investigating the Fermi–Pasta–Ulam–Tsingou problem, found that solitary waves had the elastic properties of colliding particles; the waves' initial and ultimate amplitudes and velocities remained unchanged after wave collisions.[3] These particle-like waves are called solitons and arise in nonlinear equations because of a weak balance between dispersive and nonlinear effects.[3]
Gardner, Greene, Kruskal and Miura introduced the inverse scattering transform for solving the Korteweg–de Vries equation.[4] Lax, Ablowitz, Kaup, Newell, and Segur generalized this approach which led to solving other nonlinear equations including the nonlinear Schrödinger equation, sine-Gordon equation, modified Korteweg–De Vries equation, Kadomtsev–Petviashvili equation, the Ishimori equation, Toda lattice equation, and the Dym equation.[3][5][6] This approach has also been applied to different types of nonlinear equations including differential-difference, partial difference, multidimensional equations and fractional integrable nonlinear systems.[3]
Description
[edit]Nonlinear partial differential equation
[edit]The independent variables are a spatial variable and a time variable . Subscripts or differential operators () indicate differentiation. The function is a solution of a nonlinear partial differential equation, , with initial condition (value) .[2]: 72
Requirements
[edit]The differential equation's solution meets the integrability and Fadeev conditions:[2]: 40
- Integrability condition:
- Fadeev condition:
Differential operator pair
[edit]The Lax differential operators, and , are linear ordinary differential operators with coefficients that may contain the function or its derivatives. The self-adjoint operator has a time derivative and generates a eigenvalue (spectral) equation with eigenfunctions and time-constant eigenvalues (spectral parameters) .[1]: 4963 [2]: 98
- and
The operator describes how the eigenfunctions evolve over time, and generates a new eigenfunction of operator from eigenfunction of .[1]: 4963
The Lax operators combine to form a multiplicative operator, not a differential operator, of the eigenfuctions .[1]: 4963
The Lax operators are chosen to make the multiplicative operator equal to the nonlinear differential equation.[1]: 4963
The AKNS differential operators, developed by Ablowitz, Kaup, Newell, and Segur, are an alternative to the Lax differential operators and achieve a similar result.[1]: 4964 [7][8]
Direct scattering transform
[edit]The direct scattering transform generates initial scattering data; this may include the reflection coefficients, transmission coefficient, eigenvalue data, and normalization constants of the eigenfunction solutions for this differential equation.[2]: 39–48
Scattering data time evolution
[edit]The equations describing how scattering data evolves over time occur as solutions to a 1st order linear ordinary differential equation with respect to time. Using varying approaches, this first order differential equation may arise from the the Lax operators, a combination of Lax operators and the nonlinear differential equation, or through additional substitution, integration or differentiation operations. Spatially asymptotic equations () simplify solving these differential equations.[1]: 4967–4968 [2]: 68–72 [4]
Inverse scattering transform
[edit]The Marchenko equation combines the scattering data into a linear Fredholm integral equation. The solution to this integral equation leads to the solution, u(x,t), of the nonlinear differential equation.[2]: 48–57
Example: Korteweg–De Vries equation
[edit]The nonlinear differential Korteweg–De Vries equation is [9]: 4
Lax operators
[edit]The Lax operators are:[2]: 97–102
- and
The multiplicative operator is:
Direct scattering transform
[edit]The solutions to this differential equation
may include scattering solutions with a continuous range of eigenvalues (continuous spectrum) and bound-state solutions with discrete eigenvalues (discrete spectrum). The scattering data includes transmission coefficients , left reflection coefficient , right reflection coefficient , discrete eigenvalues , and left and right bound-state normalization (norming) constants.[1]: 4960
Scattering data time evolution
[edit]The spatially asymptotic left and right Jost functions simplify this step.[1]: 4965–4966
The dependency constants relate the right and left Jost functions and right and left normalization constants.[1]: 4965–4966
The Lax differential operator generates an eigenfunction which can be expressed as a time-dependent linear combination of other eigenfunctions.[1]: 4967
The solutions to these differential equations, determined using scattering and bound-state spatially asymptotic Jost functions, indicate a time-constant transmission coefficient , but time-dependent reflection coefficients and normalization coefficients.[1]: 4967–4968
Inverse scattering transform
[edit]The Marchenko kernel is .[1]: 4968–4969
The Marchenko integral equation is a linear integral equation solved for .[1]: 4968–4969
The solution to the Marchenko equation, , generates the solution to the nonlinear partial differential equation.[1]: 4969
Examples of integrable equations
[edit]- Korteweg–de Vries equation
- nonlinear Schrödinger equation
- Camassa-Holm equation
- Sine-Gordon equation
- Toda lattice
- Ishimori equation
- Dym equation
See also
[edit]Citations
[edit]References
[edit]- Ablowitz, M. J.; Kaup, D. J.; Newell, A. C.; Segur, H. (1973). "Method for Solving the Sine-Gordon Equation". Physical Review Letters. 30 (25): 1262–1264. doi:10.1103/PhysRevLett.30.1262.
- Ablowitz, M.J.; Kaup, D.J.; Newell, A.C.; Segur, H. (1974). "The Inverse Scattering Transform—Fourier Analysis for Nonlinear Problems". Studies in Applied Mathematics. 53: 249–315.
- Ablowitz, Mark J.; Segur, Harvey (1981). Solitons and the Inverse Scattering Transform. SIAM. ISBN 978-0-89871-477-7.
- Ablowitz, Mark J. (2023). "Nonlinear waves and the Inverse Scattering Transform". Optik. 278: 170710. doi:10.1016/j.ijleo.2023.170710.
- Aktosun, Tuncay (2009). "Inverse Scattering Transform and the Theory of Solitons". Encyclopedia of Complexity and Systems Science. Springer. pp. 4960–4971. ISBN 978-0-387-30440-3.
- Drazin, P. G.; Johnson, R. S. (1989). Solitons: An Introduction. Cambridge University Press. ISBN 978-0-521-33655-0.
- Gardner, Clifford S.; Greene, John M.; Kruskal, Martin D.; Miura, Robert M. (1967). "Method for Solving the Korteweg-deVries Equation". Physical Review Letters. 19 (19): 1095–1097. doi:10.1103/PhysRevLett.19.1095.
- Konopelchenko, B.G.; Dubrowsky, V.G. (1991). "Localized solitons for the Ishimori equation". In Sattinger, David H.; Tracy, C.A.; Venakides, Stephanos (eds.). Inverse Scattering and Applications. American Mathematical Soc. pp. 77–90. ISBN 978-0-8218-5129-6.
- Oono, H. (1996). "N-Soliton solution of Harry Dym equation by inverse scattering method.". In Alfinito, E.; Boiti, M.; Martina, L. (eds.). Nonlinear Physics: Theory and Experiment. World Scientific Publishing Company Pte Limited. pp. 241–248. ISBN 978-981-02-2559-9.
Further reading
[edit]- Ablowitz, Mark J.; Clarkson, P. A. (12 December 1991). Solitons, Nonlinear Evolution Equations and Inverse Scattering. Cambridge University Press. ISBN 978-0-521-38730-9.
- Bullough, R. K.; Caudrey, P. J. (11 November 2013). Solitons. Springer Science & Business Media. ISBN 978-3-642-81448-8.
- Gardner, Clifford S.; Greene, John M.; Kruskal, Martin D.; Miura, Robert M. (1974), "Korteweg-deVries equation and generalization. VI. Methods for exact solution.", Comm. Pure Appl. Math., 27: 97–133, doi:10.1002/cpa.3160270108, MR 0336122
- Gelʹfand, Izrailʹ Moiseevich (1955). On the Determination of a Differential Equation from Its Spectral Function. American Mathematical Society. p. 253-304.
- Marchenko, Vladimir A. (1986). Sturm-Liouville Operators and Applications. Basel: Birkhäuser.
- Shaw, J. K. (1 May 2004). Mathematical Principles of Optical Fiber Communication. SIAM. ISBN 978-0-89871-556-9.