Abstract
We consider self-propelled particles confined between two parallel plates, moving with a constant velocity while their moving direction changes by rotational diffusion. The probability distribution of such micro-organisms in confined environment is singular because particles accumulate at the boundaries. This leads us to distinguish between the probability distribution densities in the bulk and in the boundaries. They satisfy a degenerate Fokker–Planck system and we propose boundary conditions that take into account the switching between free-moving and boundary-contacting particles. Relative entropy property, a priori estimates and the convergence to an unique steady state are established. The steady states of both the PDE and individual based stochastic models are compared numerically.
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Communicated by Julien Tailleur.
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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
B.P. has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No 740623). M. Tang is supported by NSFC 11871340 and Young Changjiang Scholar of Ministry of Education in China.
Appendix: Discrete Relative Entropy Inequality and a Priori Estimate
Appendix: Discrete Relative Entropy Inequality and a Priori Estimate
The semi-discrete relative gap \(\omega _{i,j}(t)\) and \(\omega _{\pm ,j}(t)\) are defined by
Here we state the discrete relative entropy inequality which is satisfied by discrete density \(p_{i,j}(t)\) and \(p_{\pm ,j}(t)\).
Theorem 6.1
For any convex function \(H\in \mathcal {C}^2(\mathbb {R})\), semi-discrete relative gap \(\omega _{i,j}(t)\) and \(\omega _{\pm ,j}(t)\) satisfy the relative entropy inequality:
where equality holds if and only if \(\omega _{i,j}=0\) for any \(i \in V_{y}\), \(j \in V_{\theta }\) and \(\omega _{\pm ,j}=0\) for any \(j \in V_{\theta ,\pm }\).
Proof
As in Sect. 2, we divide the proof in several steps.
First step: estimate of relative entropy at BCP. Substitute \(p_{+,j}\) by \(q_{+,j}\omega _{+,j}+q_{+,j}\) in (4.2d) and employ (4.6d), we deduce that
Notice that
by rearranging \(H'(\omega _{+,j})\)(6.3)\(-\frac{D_{\theta }}{\Delta \theta ^2}\)(6.4), it follows that
where equality holds if and only if \(\omega _{+,j-1}=\omega _{+,j}=\omega _{+,j+1}=\omega _{2I,j}\).
Sum up (6.5) for \(j\in V_{\theta _+}\), we have
where equality holds if and only if \(\omega _{+,j}=\omega _{2I,j}=0\) for any \(j\in V_{\theta _+}\).
Similarly, we obtain an inequality for \(q_{-,j}H(\omega _{-,j})\)
where equality holds if and only if \(\omega _{-,j}=\omega _{1,j}=0\) for any \(j\in V_{\theta _-}\).
Second Step: Estimate of Relative Entropy at FSP Substituting \(p_{i,j}\) by \(q_{i,j}\omega _{i,j}+q_{i,j}\) in (4.2a) we obtain that for any \(i\in V_y\), \(j\in V_{\theta _+}\),
By (4.6a), the last term is zero. Rearranging \(H'(\omega _{i,j})\)(6.8), we deduce
The last two terms offset according to (4.6a), so we deduce that
holds for \(i \in V_y\), \(j \in V_{\theta _+}\), where equality holds if and only if \(\omega _{i,j}=\omega _{i,j-1}=\omega _{i,j+1}=\omega _{i-1,j}\).
Similarly, by rearranging (4.2a) and employ (4.6b), we have
holds for \(i \in V_y \), \(j \in V_{\theta _-}\), where equality holds if and only if \(\omega _{i,j}=\omega _{i,j-1}=\omega _{i,j+1}=\omega _{i+1,j}\).
For \(i \in V_y\setminus \{1,2I\}\), \(j\in V_{\theta _0}\), (4.2c) and (4.6a) gives
where equality holds if and only if \(\omega _{i,j}=\omega _{i,j-1}=\omega _{i,j+1}\).
Third step: special case where \(\frac{\partial p}{\partial \theta }\) jumps. On node \((i,j)=(2I,J)\), substitute \(p_{i,j}\) by \(q_{i,j}\omega _{i,j}+q_{i,j}\) in (4.4c) we obtain
in which the last term is zero by (4.7). Multiply both sides with \(H'(\omega _{2I,J})\), it follows that
The last term is zero according to (4.7), so we deduce that
where equality holds if and only if \(\omega _{2I,J-1}=\omega _{2I,J}=\omega _{2I,J+1}=\omega _{+,J+1}\).
Similarly, on node \((i,j)=(2I,2J),(1,0),(1,J)\), we have
Fourth step: collection and offsets To summarize, sum up (6.9) for \(i \in V_y\), \(j \in V_{\theta _+}\), (6.10) for \(i \in V_y\), \(j \in V_{\theta _-}\), (6.11) for \(i \in V_y\setminus \{1,2I\}\), \(j \in V_{\theta _0}\), together with (6.13)–(6.16), the diffusion term offsets and it follows that
Hence (6.5), (6.7) and (6.17) imply inequality (6.2) in the theorem.
Define
Choose \(H(x)=x^2\) in (6.2) then we have \(\mathrm{d}M(t) / \, \mathrm{d} t\le 0\). Suppose the initial data of \(\omega _{i,j}\) and \(\omega _{\pm ,j}\) satisfies \(M(0)<\infty \), then since M(t) decreases monotonically, and it has a lower bound 0, so M(t) converges as t goes to infinity. While the equality of (6.2) holds if and only if \(M(t)=0\), so we have \(\lim _{t\rightarrow \infty }M(t)=0\), which implies \(\lim _{t\rightarrow \infty }\omega _{i,j}(t)=\lim _{t\rightarrow \infty }\omega _{\pm ,j}(t)=0.\) We conclude that \(p_{i,j}(t)\) converges to \(q_{i,j}\) for any \(i \in V_\theta \), \(j \in V_y\) as t goes to infinity, and \(p_{\pm ,j}(t)\) converges to \(q_{\pm ,j}\) for any \(j \in V_{j,\pm }\) as t goes to infinity.
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Fu, J., Perthame, B. & Tang, M. Fokker–Plank System for Movement of Micro-organism Population in Confined Environment. J Stat Phys 184, 1 (2021). https://doi.org/10.1007/s10955-021-02760-y
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DOI: https://doi.org/10.1007/s10955-021-02760-y
Keywords
- Piecewise deterministic Markov process
- Fokker–Planck equation
- Relative entropy
- Monte–Carlo simulations
- Semi-discrete numerical schemes