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Carleman Estimates and Controllability for a Degenerate Structured Population Model

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Abstract

In this paper we study the null controllability property for a single population model in which the population y depends on time t, space x, age a and size \(\tau \). Moreover, the diffusion coefficient k is degenerate at a point of the domain or both extremal points. Our technique is essentially based on Carleman estimates. The \(\tau \) dependence requires us to modify the weight for the Carleman estimates, and accordingly the proof of the observability inequality. Thanks to this observability inequality we obtain a null controllability result for an intermediate problem and finally for the initial system through suitable cut off functions.

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Correspondence to Genni Fragnelli.

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The first author is a member of the Gruppo Nazionale per l’Analisi Matematica, la Probabilità e le loro Applicazioni (GNAMPA) of the Istituto Nazionale di Alta Matematica (INdAM) and she is supported by the FFABR Fondo per il finanziamento delle attività base di ricerca 2017, by the INdAM- GNAMPA Project 2019 Controllabilità di PDE in modelli fisici e in scienze della vita, by Fondi di Ateneo 2015/16 of the University of Bari Problemi differenziali non linearii and by PRIN 2017-2019 Qualitative and quantitative aspects of nonlinear PDEs.

The second author is supported by Grant-in-Aid for Scientific Research (S) 15H05740 of Japan Society for the Promotion of Science and by The National Natural Science Foundation of China (No. 11771270, 91730303), and prepared with the support of the “RUDN University Program 5-100”

Appendix

Appendix

1.1 Proof of Proposition 4.2

Let us consider a smooth function \(\xi : [0,1] \rightarrow \mathbb R\) such that

$$\begin{aligned} {\left\{ \begin{array}{ll} 0 \le \xi (x) \le 1, &{} \text { for all } x \in [0,1], \\ \xi (x) = 1 , &{} x \in \omega ', \\ \xi (x)=0, &{} x \in (0,1)\setminus \omega . \end{array}\right. } \end{aligned}$$

Then, integrating by parts one has

$$\begin{aligned} \begin{aligned} 0 =&\int _0^T\!\!\!\frac{d}{dt}\left( \int _{Q_{A, \tau _2,1}}(\xi e^{s\psi })^2v^2dxd \tau da\right) dt \\&= \int _Q 2s\psi _t(\xi e^{s\psi })^2v^2 + 2(\xi e^{s\psi })^2v(-v_a-gv_\tau -\mathcal A_0 v+ \mu v+f) \,dxd \tau dadt \\&=\; 2s\int _Q \psi _t(\xi e^{s\psi })^2v^2dxd\tau da dt\\&\quad + 2s\int _Q \psi _a(\xi e^{s\psi })^2v^2dxd\tau da dt+ 2\int _Q \left( \xi ^2e^{2s\psi }\sigma \right) _x\eta vv_xdxd\tau da dt \\&\quad + 2\int _Q (\xi ^2e^{2s\psi }k)v_x^2dxd\tau da dt+2\int _Q \xi ^2e^{2s\psi }\mu v^2dxd\tau da dt \\&\quad +2\int _Q \xi ^2e^{2s\psi }fvdxd\tau da dt\\&\quad + 2s \int _Q \psi _\tau (\xi e^{s\psi })^2gv^2dxd\tau da dt + \int _Q \xi ^2 e^{2s\psi }g_\tau v^2dxd\tau da dt. \end{aligned} \end{aligned}$$

Hence, using Young’s inequality

$$\begin{aligned}&2\int _Q (\xi ^2e^{2s\psi }k)v_x^2dxd\tau da dt \\&\quad = -2s\int _Q \psi _t\left( \xi e^{s\psi }\right) ^2v^2dxd\tau da dt- 2s\int _Q \psi _a(\xi e^{s\psi })^2v^2dxd\tau da dt \\&\qquad -2\int _Q\frac{ \left( \xi ^2e^{2s\psi }\sigma \right) _x}{\xi e^{s\psi }\sqrt{\sigma }}\xi e^{s\psi }\sqrt{\sigma }\eta vv_x\,dxd\tau da dt-2\int _Q \xi ^2e^{2s\psi }\mu v^2dxd\tau da dt\\&\qquad -2\int _Q \xi ^2e^{2s\psi }fvdxd\tau da dt\\&\qquad -2s \int _Q \psi _\tau (\xi e^{s\psi })^2gv^2dxd\tau da dt -\int _Q \xi ^2 e^{2s\psi }g_\tau v^2dxd\tau da dt \\&\quad \le -2s\int _Q \psi _t(\xi e^{s\psi })^2v^2dxd\tau da dt - 2s\int _Q \psi _a(\xi e^{s\psi })^2v^2dxd\tau da dt\\&\qquad -2s \int _Q \psi _\tau (\xi e^{s\psi })^2gv^2dxd\tau da dt -\int _Q \xi ^2 e^{2s\psi }g_\tau v^2dxd\tau da dt\\&\qquad + 4\int _Q \left( \xi e^{s\psi }\sqrt{\sigma }\,\right) _x^2\eta v^2dxd\tau da dt+\int _Q (\xi ^2e^{2s\psi }k)v_x^2dxd\tau da dt \\&\qquad +(2\Vert \mu \Vert _{L^\infty (Q)}+1)\int _Q \xi ^2v^2dxd\tau da dt +\int _Q \xi ^2e^{2s\psi }f^2dxd\tau da dt. \end{aligned}$$

It follows that,

$$\begin{aligned} \begin{aligned}&\int _Q (\xi ^2e^{2s\psi }k)v_x^2dxd\tau da dt\\&\quad \le -2s\int _Q \psi _t(\xi e^{s\psi })^2v^2dxd\tau da dt - 2s\int _Q \psi _a(\xi e^{s\psi })^2v^2dxd\tau da dt\\&\qquad -2s \int _Q \psi _\tau (\xi e^{s\psi })^2gv^2dxd\tau da dt -\int _Q \xi ^2 e^{2s\psi }g_\tau v^2dxd\tau da dt\\&\qquad + 4\int _Q \left( \xi e^{s\psi }\sqrt{\sigma }\,\right) _x^2\eta v^2dxd\tau da dt \\&\qquad +(2\Vert \mu \Vert _{L^\infty (Q)}+1)\int _Q \xi ^2v^2dxd\tau da dt +\int _Q \xi ^2e^{2s\psi }f^2dxd\tau da dt. \end{aligned} \end{aligned}$$

Thus,

$$\begin{aligned}&\inf _{\omega '}\{k\}\int _{Q_{T,A, \tau _2}}\int _{\omega '}e^{2s\psi }v_x^2dxd\tau da dt \nonumber \\&\quad \le \left( \sup _{\omega \times (0,T)}\Big \{\left| 4\left( \xi e^{s\psi }\sqrt{\sigma }\,\right) _x^2-2s(\psi _t+\psi _a+ \psi _\tau g+g_\tau ) (\xi e^{s\psi })^2\right| \Big \} +\, 2\Vert \mu \Vert _{L^\infty (Q)}+1\right) \\&\qquad \int _{Q_{T,A, \tau _2}}\int _{\omega }v^2dxd\tau da dt +\int _Q f^2e^{2s\psi } dxd\tau da dt. \end{aligned}$$

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Fragnelli, G., Yamamoto, M. Carleman Estimates and Controllability for a Degenerate Structured Population Model. Appl Math Optim 84, 999–1044 (2021). https://doi.org/10.1007/s00245-020-09669-0

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