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Machine Learning methods in incompressible fluid dynamics

Authors: Prat Colomer, Maria;

Machine Learning methods in incompressible fluid dynamics

Abstract

Encontrar soluciones de EDPs reformulándolas en un problema de minimización es una tarea difícil debido a la presencia de un número infinito de mínimos locales y a la no convexidad genérica del funcional. En esta tesis de máster, utilizamos redes neuronales informadas por la física (PINNs) para encontrar nuevas soluciones no triviales a las ecuaciones de Euler incompresibles en dos dimensiones.

Trobar solucions d'EDPs reformulant-les en un problema de minimització és una tasca difícil a causa de la presència d'un nombre infinit de mínims locals i a la no convexitat genèrica del funcional. En aquesta tesi de màster, utilitzem xarxes neuronals informades per la física (PINNs) per trobar noves solucions no trivials a les equacions d'Euler incompressibles en dues dimensions.

Finding solutions to PDEs by recasting them into a minimization problem is a hard problem due to the presence of infinitely many local minima and generic non-convexity of the functional. In this master thesis we use physics informed neural networks (PINNs) to find new, nontrivial solutions to the incompressible two-dimensional Euler equations.

Keywords

Àrees temàtiques de la UPC::Matemàtiques i estadística, machine learning, Fluid dynamics, Dinàmica de fluids, Machine learning, Aprenentatge automàtic, 2D euler equations, 500, Physics-informed neural networks, numerical methods for partial differential equations, 530, Classificació AMS::70 Mechanics of particles and systems::70K Nonlinear dynamics

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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