Abstract
A fully discrete Lagrangian scheme for numerical solution of the nonlinear fourth-order DLSS equation in one space dimension is analyzed. The discretization is based on the equation’s gradient flow structure in the \(L^2\)-Wasserstein metric. By construction, the discrete solutions are strictly positive and mass conserving. A further key property is that they dissipate both the Fisher information and the logarithmic entropy. Our main result is a proof of convergence of fully discrete to weak solutions in the limit of vanishing mesh size. Convergence is obtained for arbitrary nonnegative, possibly discontinuous initial data with finite entropy, without any CFL-type condition. The key estimates in the proof are derived from the dissipations of the two Lyapunov functionals. Numerical experiments illustrate the practicability of the scheme.
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Acknowledgments
The authors are indebted to Giuseppe Savaré for fruitful discussions on the subject, and especially for contributing the initial idea for the entropy preserving discretization scheme. We further thank the anonymous referees for their extremely careful reading of the initial manuscript and their suggestions for improvement.
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Communicated by Eitan Tadmor.
This research was supported by the DFG Collaborative Research Center TRR 109, “Discretization in Geometry and Dynamics”.
Appendices
Appendix 1: Some technical lemmas
Lemma 28
For each \(p>1\) and \(\vec {\mathrm {x}}\in \mathfrak {x}_\delta \) with \(\vec {\mathrm {z}}=\mathbf {z}[\vec {\mathrm {x}}]\), one has that
Proof
The first equality is simply the definition (22) of \(z_\kappa \). Since trivially \(x_{\kappa +\frac{1}{2}}-x_{\kappa -\frac{1}{2}}<b-a\) for each \(\kappa \in {\mathbb {I}_K^{1/2}}\), and since \(p-1>0\), it follows that
\(\square \)
Lemma 29
For each \(\vec {\mathrm {x}}\in \mathfrak {x}_\delta \) with \(\vec {\mathrm {z}}=\mathbf {z}[\vec {\mathrm {x}}]\), one has that
and consequently,
Proof
The first estimate in (100) is an immediate consequence of the definition of \(z_\kappa \) in (22). To prove the second estimate, let \(\kappa ^*\in {\mathbb {I}_K^{1/2}}\) be such that \(z_{\kappa ^*}=\max z_k\). Observe that there exists a \(\kappa _*\in {\mathbb {I}_K^{1/2}}\) such that
Writing out \(z_{\kappa ^*}-z_{\kappa _*}\) as a sum over differences of adjacent values of \(z_k\) and applying the triangle and Cauchy Schwarz inequality, one obtains
Now combine this with (102). \(\square \)
Lemma 30
With \(\widehat{u}\) and \(\bar{u}\) being, respectively, the piecewise linear and the piecewise constant densities associated with a given vector \(\vec {\mathrm {x}}\), then
Proof
First observe that
which is an easy consequence of a Taylor expansion for the function \(s\mapsto (1+s)\ln s\) around \(s=1\), substituting \(s=p/q\). On the one hand, we have that
and on the other hand,
where we have used (104). This clearly implies (103). \(\square \)
Lemma 31
(Gargliardo–Nirenberg inequality). For each \(f\in H^1([a,b])\), one has that
Proof
Assume first that \(f\ge 0\). Then, for arbitrary \(a<x<y<b\), the fundamental theorem of calculus and Hölder’s inequality imply that
Since \(f\ge 0\), we can further estimate
This shows (105) for nonnegative functions f. A general f can be written in the form \(f=f_+-f_-\), where \(f_\pm \ge 0\). By the triangle inequality, and since \(\Vert f_\pm \Vert _{H^1([a,b])}\le \Vert f\Vert _{H^1([a,b])}\),
This proves the claim. \(\square \)
Appendix 2: Proof of Lemma 23
Proof of estimate (76)
First, observe that by definition of \(\widehat{z}\),
and therefore, by Hölder’s inequality,
with, recalling (41),
To simplify \(R_{1,\beta }\), let us fix n, and introduce \(\tilde{x}_k^+\in (x_k^n,x_{k+1}^n)\) and \(\tilde{x}_k^-\in (x_{k-1}^n,x_k^n)\) such that
For each \(k\in {\mathbb {I}_K^+}\), we have that—recalling (70)—
Since \(\mathrm {X}_\Delta ^n(\xi )\in [x_k^n,x_{k+\frac{1}{2}}^n]\) for each \(\xi \in [\xi _k,\xi _{k+\frac{1}{2}}]\), and \(\tilde{x}_k^+\in [x_k^n,x_{k+1}^n]\), it follows that \(|\mathrm {X}_\Delta ^n(\xi )-\tilde{x}_k^+|\le x_{k+1}^n-x_k^n\), and therefore,
A similar estimate holds for the other integral. Thus,
Combining the estimate (43) with inequality (99) from the “Appendix,” we further conclude that
In combination with (106) and (107), this proves the claim. \(\square \)
Proof of estimate (77)
The proof is almost identical to (and even easier than) the one for estimate (76) above. Again, we have a decomposition of the form
where \(R_{2,\alpha }\) equals \(R_{1,\alpha }\) from (107), and
By writing
and observing—in analogy to (108)—that
we obtain the same bound on \(R_{2,\beta }\) as the one on \(R_{1,\beta }\) from (109). \(\square \)
Proof of estimate (78)
Arguing like in the previous proofs, we first deduce—now by means of Hölder’s inequality instead of the Cauchy–Schwarz inequality—that
where \(R_{3,\alpha }=R_{1,\alpha }\), and
Introduce intermediate values \(\tilde{x}_k^+\) such that
Thus, we have that
By the analogue of (108), it follows further that
where we have used (99) from the “Appendix.” At this point, the estimates (42) and (43) are used to control the first and the second sum, respectively. \(\square \)
Proof of estimate (79)
Here, one proceeds in full analogy to the proof of estimate (78) above. \(\square \)
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Matthes, D., Osberger, H. A Convergent Lagrangian Discretization for a Nonlinear Fourth-Order Equation. Found Comput Math 17, 73–126 (2017). https://doi.org/10.1007/s10208-015-9284-6
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DOI: https://doi.org/10.1007/s10208-015-9284-6