Noticias en español
Group actions with discrete spectrum and their amorphic complexity.
Abstract: Amorphic complexity, originally introduced for integer actions, is a topological invariant which measures the complexity of dynamical systems in the regime of zero entropy. We will introduce its definition for actions by locally compact sigma-compact amenable groups on compact metric spaces. Further, we will illustrate some of its basic properties and show why it is tailor-made to study strictly ergodic group actions with discrete spectrum and continuous eigenfunctions. This class of actions includes, in particular, Delone dynamical systems related to regular model sets obtained...
Read MoreTurbulent steady states in the nonlinear Schrodinger equation.
Abstract: The nonlinear Schrodinger (NLS) equation, also known as the Gross-Pitaevskii equation, is one of the most common equations in physics. Its applications go from the propagation of light in nonlinear media to the description of gravity waves and Bose-Einstein condensates. In general, the NLS equation describes the evolution of nonlinear waves. Such waves interact and transfer energy and other invariants along scales in a cascade process. This phenomenon is known as wave turbulence and is described by the (weak) wave turbulence theory (WWT). One of the most significant achievements of...
Read MoreEvaluación del coeficiente de atenuación difusa de la radiación fotosintéticamente activa en el lago Villarrica.
Resumen: “El coeficiente de atenuación difusa de la radiación fotosintéticamente activa es una propiedad óptica inherente importante del campo de luz subacuático. Este parámetro, como medida de la transparencia del medio, es un buen indicador de la calidad del agua. En este estudio, se utilizaron imágenes Landsat 8 OLI y Sentinel-2A/B MSI basadas en algoritmos para estimar el coeficiente de atenuación difusa de la radiación fotosintéticamente activa en un lago en el centro-sur de Chile. Los datos estimados de los algoritmos del módulo ACOLITE se validaron con mediciones in situ de seis...
Read MoreDirichlet-to-Neumann and Calderon operator via deep learning techniques.
Abstract: In this talk we consider the Dirichlet-to-Neumann operator and the Calderón mapping appearing in Calderon’s inverse problem. Using deep learning techniques, we prove that these maps are rigorously approximated by infinite-dimensional neural networks.
Read MoreLong time asymptotics of large data in the Kadomtsev-Petviashvili models and geometrical aspects of its dynamics.
Abstract: In this talk we consider the Kadomtsev-Petviashvili equations posed on R2. For both models, we provide sequential in time asymptotic descriptions of solutions obtained from arbitrarily large initial data, inside and far regions of the plane not containing lumps or line solitons, and under minimal regularity assumptions. A geometrical description of the dynamics will be given in terms of parabolic regions.
Read MoreSimplified Kalman filtering for non-linear models.
Abstract: We will discuss the problem of approximate statistical inference in the hidden Markov models where the observation equations are non-linear. We propose a Bayesian approach based on a Gaussian approximation as well as its versions suitable for “large” problems. The proposed approach may be seen as an approximate Kalman filter which is generic in the sense that it can be used for any non-linear relationship between the hidden state and the outcome. We show how the proposed simplified Kalman filter can be used in the context of sport rating where the skills of the...
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