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1. Global solutions to the Boltzmann equation without angular cutoff and the Landau equation with Coulomb potential

   Duan, Renjun; Liu, Shuangqian; Sakamoto, Shota; Strain, Robert M. (June 2020, RIMS kokyuroku bessatsu)

   Takayoshi Ogawa; Keiichi Kato; Mishio Kawashita (Ed.)

   This report succinctly summarizes results proved in the authors' recent work (2019) where the unique existence of solutions to the Boltzmann equation without angular cut-off and the Landau equation with Coulomb potential are studied in a perturbation framework. A major feature is the use of the Wiener space $$A(\Omega)$$, which can be expected to play a similar role to $$L^\infty$$. Compared to the $L^2$-based solution spaces that were employed for prior known results, this function space enables us to establish a new global existence theory. One further feature is that, not only an initial value problem, but also an initial boundary value problem whose boundary conditions can be regarded as physical boundaries in some simple situation, are considered for both equations. In addition to unique existence, large-time behavior of the solutions and propagation of spatial regularity are also proved. In the end of report, key ideas of the proof will be explained in a concise way. 

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2. The Landau Equation with the Specular Reflection Boundary Condition.

   Guo, Yan; Hwang, Hyung Ju; Jang, Jin Woo; Ouyang, Zhimeng (January 2020, Archive for Rational Mechanics and Analysis)

   The existence and stability of the Landau equation (1936) in a general bounded domain with a physical boundary condition is a long-outstanding open problem. This work proves the global stability of the Landau equation with the Coulombic potential in a general smooth bounded domain with the specular reflection boundary condition for initial perturbations of the Maxwellian equilibrium states. The highlight of this work also comes from the low-regularity assumptions made for the initial distribution. This work generalizes the recent global stability result for the Landau equation in a periodic box (Kim et al. in Peking Math J, 2020). Our methods consist of the generalization of the wellposedness theory for the Fokker–Planck equation (Hwang et al. SIAM J Math Anal 50(2):2194–2232, 2018; Hwang et al. Arch Ration Mech Anal 214(1):183–233, 2014) and the extension of the boundary value problem to a whole space problem, as well as the use of a recent extension of De Giorgi–Nash–Moser theory for the kinetic Fokker–Planck equations (Golse et al. Ann Sc Norm Super Pisa Cl Sci 19(1):253–295, 2019) and the Morrey estimates (Bramanti et al. J Math Anal Appl 200(2):332–354, 1996) to further control the velocity derivatives, which ensures the uniqueness. Our methods provide a new understanding of the grazing collisions in the Landau theory for an initial-boundary value problem. 

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3. Correction to: The Landau Equation with the Specular Reflection Boundary

   Guo, Yan; Hwang, Hyung Ju; Jang, Jin Woo; Ouyang, Zhimeng (January 2021, Archive for rational mechanics and analysis)

   null (Ed.)
4. EikoNet: Solving the Eikonal Equation With Deep Neural Networks

   https://doi.org/10.1109/tgrs.2020.3039165  

   Smith, Jonathan D.; Azizzadenesheli, Kamyar; Ross, Zachary E. (December 2020, IEEE Transactions on Geoscience and Remote Sensing)

   null (Ed.)

   The recent deep learning revolution has created enormous opportunities for accelerating compute capabilities in the context of physics-based simulations. In this article, we propose EikoNet, a deep learning approach to solving the Eikonal equation, which characterizes the first-arrival-time field in heterogeneous 3-D velocity structures. Our grid-free approach allows for rapid determination of the travel time between any two points within a continuous 3-D domain. These travel time solutions are allowed to violate the differential equation--which casts the problem as one of optimization--with the goal of finding network parameters that minimize the degree to which the equation is violated. In doing so, the method exploits the differentiability of neural networks to calculate the spatial gradients analytically, meaning that the network can be trained on its own without ever needing solutions from a finite-difference algorithm. EikoNet is rigorously tested on several velocity models and sampling methods to demonstrate robustness and versatility. Training and inference are highly parallelized, making the approach well-suited for GPUs. EikoNet has low memory overhead and further avoids the need for travel-time lookup tables. The developed approach has important applications to earthquake hypocenter inversion, ray multipathing, and tomographic modeling, as well as to other fields beyond seismology where ray tracing is essential. 

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5. The complex Monge–Ampère equation with a gradient term

   https://doi.org/10.4310/PAMQ.2021.v17.n3.a7  

   Tosatti, Valentino; Weinkove, Ben (January 2021, Pure and Applied Mathematics Quarterly)