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Andrea L. Bertozzi
Researcher at University of California, Los Angeles
Publications - 390
Citations - 20128
Andrea L. Bertozzi is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Graph (abstract data type) & Laplacian matrix. The author has an hindex of 70, co-authored 366 publications receiving 17679 citations. Previous affiliations of Andrea L. Bertozzi include Duke University & University of Oxford.
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Book
Vorticity and incompressible flow
TL;DR: In this article, an introduction to vortex dynamics for incompressible fluid flows is given, along with vortex sheets, weak solutions and approximate-solution sequences for the Euler equation.
Proceedings ArticleDOI
Navier-stokes, fluid dynamics, and image and video inpainting
TL;DR: A class of automated methods for digital inpainting using ideas from classical fluid dynamics to propagate isophote lines continuously from the exterior into the region to be inpainted is introduced.
Journal ArticleDOI
Self-Propelled Particles with Soft-Core Interactions: Patterns, Stability, and Collapse
TL;DR: For the first time, a coherent theory is presented, based on fundamental statistical mechanics, for all possible phases of collective motion of driven particle systems, to predict stability and morphology of organization starting from the shape of the two-body interaction.
Journal ArticleDOI
Swarming patterns in a two-dimensional kinematic model for biological groups ∗
Chad M. Topaz,Andrea L. Bertozzi +1 more
TL;DR: In this paper, the authors construct a model for the motion of biological organisms experiencing social interactions and study its pattern-forming behavior in two spatial dimensions, where the social interactions are modeled in the velocity term, which is nonlocal in the population density and includes a parameter that controls the interaction length scale.
Journal ArticleDOI
The challenges of modeling and forecasting the spread of COVID-19.
TL;DR: In this article, three regional-scale models for forecasting and assessing the course of the coronavirus disease 2019 (COVID-19) pandemic are presented. But, the authors focus on early-time data and provide an accessible framework for generating policy-relevant insights into its course.