More Like this

1. Phase mixing for solutions to 1D transport equation in a confining potential

   https://doi.org/10.3934/krm.2022002  

   Chaturvedi, Sanchit; Luk, Jonathan (January 2022, Kinetic and Related Models)

   Consider the linear transport equation in 1D under an external confining potential \begin{document}$$ \Phi $$\end{document}: \begin{document}$$ \begin{equation*} {\partial}_t f + v {\partial}_x f - {\partial}_x \Phi {\partial}_v f = 0. \end{equation*} $$\end{document} For \begin{document}$$ \Phi = \frac {x^2}2 + \frac { \varepsilon x^4}2 $$\end{document} (with \begin{document}$$ \varepsilon >0 $$\end{document} small), we prove phase mixing and quantitative decay estimates for \begin{document}$$ {\partial}_t \varphi : = - \Delta^{-1} \int_{ \mathbb{R}} {\partial}_t f \, \mathrm{d} v $$\end{document}, with an inverse polynomial decay rate \begin{document}$$ O({\langle} t{\rangle}^{-2}) $$\end{document}. In the proof, we develop a commuting vector field approach, suitably adapted to this setting. We will explain why we hope this is relevant for the nonlinear stability of the zero solution for the Vlasov–Poisson system in \begin{document}$ 1 $$\end{document}D under the external potential \begin{document}$$ \Phi $$\end{document}$. 

   more » « less
2. On the well-posedness of the anisotropically-reduced two-dimensional Kuramoto-Sivashinsky Equation

   https://doi.org/10.3934/dcdsb.2021305  

   Massatt, David (January 2022, Discrete and Continuous Dynamical Systems - B)

   We address the global existence and uniqueness of solutions for the anisotropically reduced 2D Kuramoto-Sivashinsky equations in a periodic domain with initial data \begin{document}$$ u_{01} \in L^2 $$\end{document} and \begin{document}$$ u_{02} \in H^{-1 + \eta} $$\end{document} for \begin{document}$$ \eta > 0 $$\end{document}. 

   more » « less
3. Hodge and Teichmüller

   https://doi.org/10.3934/jmd.2022007  

   Kahn, Jeremy; Wright, Alex (January 2022, Journal of Modern Dynamics)

   We consider the derivative \begin{document}$$ D\pi $$\end{document} of the projection \begin{document}$$ \pi $$\end{document} from a stratum of Abelian or quadratic differentials to Teichmüller space. A closed one-form \begin{document}$$ \eta $$\end{document} determines a relative cohomology class \begin{document}$$ [\eta]_\Sigma $$\end{document}, which is a tangent vector to the stratum. We give an integral formula for the pairing of \begin{document}$$ D\pi([\eta]_\Sigma) $$\end{document} with a cotangent vector to Teichmüller space (a quadratic differential). We derive from this a comparison between Hodge and Teichmüller norms, which has been used in the work of Arana-Herrera on effective dynamics of mapping class groups, and which may clarify the relationship between dynamical and geometric hyperbolicity results in Teichmüller theory. 

   more » « less
4. Generative imaging and image processing via generative encoder

   https://doi.org/10.3934/ipi.2021060  

   Ong, Yong Zheng; Yang, Haizhao (January 2022, Inverse Problems & Imaging)

   This paper introduces a novel generative encoder (GE) framework for generative imaging and image processing tasks like image reconstruction, compression, denoising, inpainting, deblurring, and super-resolution. GE unifies the generative capacity of GANs and the stability of AEs in an optimization framework instead of stacking GANs and AEs into a single network or combining their loss functions as in existing literature. GE provides a novel approach to visualizing relationships between latent spaces and the data space. The GE framework is made up of a pre-training phase and a solving phase. In the former, a GAN with generator \begin{document}$ G $$\end{document} capturing the data distribution of a given image set, and an AE network with encoder \begin{document}$$ E $$\end{document} that compresses images following the estimated distribution by \begin{document}$$ G $$\end{document} are trained separately, resulting in two latent representations of the data, denoted as the generative and encoding latent space respectively. In the solving phase, given noisy image \begin{document}$$ x = \mathcal{P}(x^*) $$\end{document}, where \begin{document}$$ x^* $$\end{document} is the target unknown image, \begin{document}$$ \mathcal{P} $$\end{document} is an operator adding an addictive, or multiplicative, or convolutional noise, or equivalently given such an image \begin{document}$$ x $$\end{document} in the compressed domain, i.e., given \begin{document}$$ m = E(x) $$\end{document}, the two latent spaces are unified via solving the optimization problem \begin{document}$$ z^* = \underset{z}{\mathrm{argmin}} \|E(G(z))-m\|_2^2+\lambda\|z\|_2^2 $$\end{document} and the image \begin{document}$$ x^* $$\end{document} is recovered in a generative way via \begin{document}$$ \hat{x}: = G(z^*)\approx x^* $$\end{document}, where \begin{document}$$ \lambda>0 $$\end{document}$ is a hyperparameter. The unification of the two spaces allows improved performance against corresponding GAN and AE networks while visualizing interesting properties in each latent space. 

   more » « less
5. Sharp critical thresholds in a hyperbolic system with relaxation

   https://doi.org/10.3934/dcds.2021098  

   Bhatnagar, Manas; Liu, Hailiang (January 2021, Discrete & Continuous Dynamical Systems)

   null (Ed.)

   We propose and study a one-dimensional \begin{document}$$ 2\times 2 $$\end{document} hyperbolic Eulerian system with local relaxation from critical threshold phenomena perspective. The system features dynamic transition between strictly and weakly hyperbolic. For different classes of relaxation we identify intrinsic critical thresholds for initial data that distinguish global regularity and finite time blowup. For relaxation independent of density, we estimate bounds on density in terms of velocity where the system is strictly hyperbolic. 

   more » « less