Abstract
In this paper, we introduce a constructive rigorous numerical method to compute smooth manifolds implicitly defined by infinite-dimensional nonlinear operators. We compute a simplicial triangulation of the manifold using a multi-parameter continuation method on a finite-dimensional projection. The triangulation is then used to construct local charts and an atlas of the manifold in the infinite-dimensional domain of the operator. The idea behind the construction of the smooth charts is to use the radii polynomial approach to verify the hypotheses of the uniform contraction principle over a simplex. The construction of the manifold is globalized by proving smoothness along the edge of adjacent simplices. We apply the method to compute portions of a two-dimensional manifold of equilibria of the Cahn–Hilliard equation.
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Notes
By “patch centered at x,” we mean the neighborhood of simplices adjacent to x.
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Acknowledgments
The authors would like to thank the anonymous referees for helpful comments and suggestions. Part of this work was done while the third author was visiting the School of Mathematics of the Georgia Institute of Technology, whose hospitality and support are gratefully acknowledged.
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Communicated by Richard Schwartz.
Marcio Gameiro was partially supported by FAPESP Grants 2013/07460-7 and 2010/00875-9, by CNPq Grant 306453/2009-6 and by CAPES Grant BEX 3979/10-5, Brazil. Alessandro Pugliese was supported in part by INdAM–GNCS.
Appendices
Appendix 1: Convolution estimates
In this Appendix, we provide the necessary convolution estimates required to construct the radii polynomials for the Cahn–Hilliard equation studied in Sect. 5. We decided to include all formulas and proofs so that the paper is self-contained. Note, however, that these analytic convolution estimates are taken directly from [15] for estimates concerning quadratic and cubic nonlinearities.
Consider a decay rate \(q \ge 2\), a computational parameter \(M \ge 6\) and define, for \(k \ge 3\),
Lemma 6.1
For \(q \ge 2\) and \(k \ge 4\), we have
Proof
First observe that
Using the above, we define
We then obtain the following recurrence inequality
Applying the above inequality \(q-2\) times, we get
Also
Using the above inequalities, we get
\(\square \)
Define the weights by
Lemma 6.2
(Quadratic estimates). Given a decay rate \(q \ge 2\) and \(M \ge 6\). For \(k \in \mathbb {Z}\), define the quadratic asymptotic estimates \(\alpha _k^{(2)} = \alpha _k^{(2)}(q,M)\) by
and for \(k < 0\),
Then, for any \(k \in \mathbb {Z}\),
Proof
For \(k=0\),
For \(1 \le k \le M-1\), and recalling that the one-dimensional weights (2),
Finally, for \(k \ge M\), one gets from Lemma 6.1 that
\(\square \)
Lemma 6.3
For any \(k \in \mathbb {Z}\) with \(|k| \ge M \ge 6\), we have that \(\alpha _k^{(2)} \le \alpha _M^{(2)}\).
Proof
For \(k \ge 6\), the fact that \( \frac{\ln (k-1)}{k+1} \le \frac{\ln (k-2)}{k} \) implies that \(\gamma _{k+1}(q) \le \gamma _{k}(q)\). By definition of \(\alpha _k^{(2)}\), for \(|k| \ge M\), one gets that \(\alpha _k^{(2)} \le \alpha _M^{(2)}\). \(\square \)
Lemma 6.4
(Cubic estimates). Given \(q \ge 2\) and \(M \ge 6\). Let
\(\tilde{\alpha }_{M}^{(2)} := \max \left\{ \alpha _k^{(2)} \mid k = 0, \ldots , M \right\} \), \(\Sigma _b^* := \tilde{\alpha }_{M}^{(2)} \gamma _M \) and \(\Sigma ^* := \min \left\{ \Sigma _a^*, \Sigma _b^* \right\} \). Define the cubic asymptotic estimates \(\alpha _k^{(3)} = \alpha _k^{(3)}(s,M)\) by
and for \(k < 0\),
Then, for any \(k \in \mathbb {Z}\),
Moreover, \(\alpha _k^{(3)} \le \alpha _M^{(3)}\), for all \(k \ge M\).
Proof
In what follows, the estimates are obtained similarly as in the proof of Lemma 6.2 with the difference that we often use the fact \(\alpha _k^{(2)} \le \alpha _M^{(2)}\), for all \(k \ge M\) (see, e.g., Remark A.1 in [14]). For \(k=0\),
For \(k > 0\),
Consider \(k \in \{ 1, \ldots , M-1 \}\). Since \(\alpha _k^{(2)} \le \alpha _M^{(2)}\), for all \(k \ge M\) by Lemma 6.3, we have
Similarly,
From the definition of \(\alpha _k^{(3)}\) for \(k \in \{1, \ldots ,M-1\}\), one gets that
For \(k \ge M\), using again that \(\alpha _k^{(2)} \le \alpha _M^{(2)}\) by Lemma 6.3, one gets that
Using Lemma 6.1,
Hence,
Recalling that \(\Sigma ^* = \min \left\{ \Sigma _a^*, \Sigma _b^* \right\} \), one gets that \( \displaystyle \sum \nolimits _{k_1=1}^{k-1} \frac{\alpha _{k_1}^{(2)}}{\omega _{k_1}^q \omega _{k-k_1}^q} \le \frac{1}{\omega _k^q} \Sigma ^*\). Also,
Combining the above inequalities, we get, for the case \(k \ge M\),
\(\square \)
Lemma 6.5
For any \(k \in \mathbb {Z}\) with \(|k| \ge M \ge 6\), we have that \(\alpha _k^{(3)} \le \alpha _M^{(3)}\).
Proof
For \(k \ge 6\), the fact that \( \frac{\ln (k-1)}{k+1} \le \frac{\ln (k-2)}{k} \) implies that \(\gamma _{k+1}(q) \le \gamma _{k}(q)\). By definition of \(\alpha _k^{(3)}\), for \(|k| \ge M\), one gets that \(\alpha _k^{(3)} \le \alpha _M^{(3)}\). \(\square \)
Lemma 6.6
Given \(q \ge 2\) and \(6 \le {\bar{M}}\le M\), define for \(0 \le k \le {\bar{M}}-1\)
and for \(k < 0\)
Fix \(0 \le |k| \le {\bar{M}}-1\) and \(\ell \in \{1,2,3\}\). Then, we have that
Proof
We have that
and
\(\square \)
Appendix 2: Coefficients Used to Define the Bounds Y and Z
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Gameiro, M., Lessard, JP. & Pugliese, A. Computation of Smooth Manifolds Via Rigorous Multi-parameter Continuation in Infinite Dimensions. Found Comput Math 16, 531–575 (2016). https://doi.org/10.1007/s10208-015-9259-7
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DOI: https://doi.org/10.1007/s10208-015-9259-7