Uncertainty is all around us and caused, for example, by the nature of a problem as in quantum mechanics, the lack of our precise knowledge as in porous media, or inaccuracies in measurements as in experiments with imperfect equip...
Uncertainty is all around us and caused, for example, by the nature of a problem as in quantum mechanics, the lack of our precise knowledge as in porous media, or inaccuracies in measurements as in experiments with imperfect equipment. While traditionally and due to the lack of computing power, science and technology relied on deterministic models, recent developments allow to include randomness. This trend requires efficient simulation methods for models with uncertainty. In space-time problems such as moving biological cells and the surface of the ocean, the randomness could be modeled by a stochastic process given explicitly or described by stochastic PDEs. Fast and accurate methods for sampling the stochastic processes are the key when computing statistical quantities of the advanced models.The main contribution of the project is the development of a theoretical framework for evolving stochastic manifolds and their efficient simulation with analyzed algorithms. Special emphasis is paid to the situation when the evolving stochastic manifold is a moving surface disturbed by external forces and described by stochastic PDEs. The main steps of the project are divided into three objectives: Obj. (A) From random fields on manifolds to stochastic manifolds. Obj. (B) From stochastic processes to evolving stochastic manifolds. Obj. (C) Solving PDEs on stochastic manifolds.The challenges are tackled based on recent advances in the simulation of Gaussian random fields on manifolds and their analysis obtained by the research team of the PI. This new breakthrough paves the way for the development of sampling methods for stochastic processes on manifolds and ultimately to evolving stochastic manifolds.To reach these goals, the PI's research group is complemented by specialists in geometric numerical integration, numerical methods for (stochastic) PDEs, and spatial statistics.ver más
02-11-2024:
Generación Fotovolt...
Se ha cerrado la línea de ayuda pública: Subvenciones destinadas al fomento de la generación fotovoltaica en espacios antropizados en Canarias, 2024
01-11-2024:
ENESA
En las últimas 48 horas el Organismo ENESA ha otorgado 6 concesiones
01-11-2024:
FEGA
En las últimas 48 horas el Organismo FEGA ha otorgado 1667 concesiones
Seleccionando "Aceptar todas las cookies" acepta el uso de cookies para ayudarnos a brindarle una mejor experiencia de usuario y para analizar el uso del sitio web. Al hacer clic en "Ajustar tus preferencias" puede elegir qué cookies permitir. Solo las cookies esenciales son necesarias para el correcto funcionamiento de nuestro sitio web y no se pueden rechazar.
Cookie settings
Nuestro sitio web almacena cuatro tipos de cookies. En cualquier momento puede elegir qué cookies acepta y cuáles rechaza. Puede obtener más información sobre qué son las cookies y qué tipos de cookies almacenamos en nuestra Política de cookies.
Son necesarias por razones técnicas. Sin ellas, este sitio web podría no funcionar correctamente.
Son necesarias para una funcionalidad específica en el sitio web. Sin ellos, algunas características pueden estar deshabilitadas.
Nos permite analizar el uso del sitio web y mejorar la experiencia del visitante.
Nos permite personalizar su experiencia y enviarle contenido y ofertas relevantes, en este sitio web y en otros sitios web.