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The SAV step is uniquely solvable and dissipates a modified energy
Statement
Under the finite-dimensional hypotheses of First-order scalar-auxiliary-variable Cahn–Hilliard step, the displayed SAV linear system has a unique solution at every step. Every solution conserves the discrete mean and obeys where , , and Thus is nonincreasing. This conclusion is about the modified energy; it does not assert monotonicity of the original physical energy .
Facts & Assumptions
Given: A finite-dimensional no-flux realization, , a previous pair , and the SAV equations.
First-order scalar-auxiliary-variable Cahn–Hilliard step: is fixed by , is fixed, and the three displayed equations are linear in .
Kernel, range, and mean-zero inverse for the no-flux Laplacian: is self-adjoint and nonnegative, annihilates constants, and its quadratic form vanishes only on constants.
Proof
Subtract two candidate solutions with the same old data. Their difference satisfies the same linear system with old data zero and the same fixed . Pair with , pair with , and use . This gives . All terms are nonnegative by [F2], so is constant. The first equation makes mean zero, hence ; the third then gives , and the second gives . Thus the square finite-dimensional linear system has trivial kernel and is invertible.
For an actual step, pairing the first SAV equation with and using proves . Pairing that equation with gives . Pairing the second equation with and using the third gives Since , equating the two expressions proves the stated modified-energy identity.
Depends on
Used by
Cited to discharge well-definedness by First-order scalar-auxiliary-variable Cahn–Hilliard step.
Dependency tree · two levels
5 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Jie Shen, Jie Xu, and Jiang Yang, The scalar auxiliary variable (SAV) approach for gradient flows, Journal of Computational Physics 353 (2018), 407–416 (standard reference, not scraped)
- Jie Shen and Xiaofeng Yang, Numerical approximations of Allen-Cahn and Cahn-Hilliard equations, Discrete and Continuous Dynamical Systems A 28 (2010), 1669–1691 (standard reference, not scraped)