Add to ORKGThis master thesis explores ways to apply geometric deep learning to the field of numerical simulations with an emphasis on the Navier-Stokes equations. With the recent success of Deep Learning, there should be room for experimentation also in the field of fluid simulations. Here we lead such an experiment. We propose an end-to-end differentiable architecture that allow object-to-mesh predictions of fluid simulations. We provide a comparison with a baseline and visual results on three different datasets: airfoils, backward facing steps and winged drones
A convolution neural network (CNN)-based approach for the construction of reduced order surrogate mo...
We propose the geometry-informed neural operator (GINO), a highly efficient approach to learning the...
Despite several advancements in experimental and computational resources, and despite progress in th...
A study to analyze the efficacy of two novel, state-of-the-art deep learning methods used in flow-fi...
In this thesis we explore machine and deep learning approaches that address keychallenges in high di...
Computational fluid dynamics (CFD) is the de-facto method for solving the Navier-Stokes equations, t...
The main objective of this thesis was to explore the capabilities of neural networks in terms of rep...
Determining the behavior of fluids is of interest in many fields. In this work, we focus on incompr...
The usage of neural networks (NNs) for flow reconstruction (FR) tasks from a limited number of senso...
The modeling of complex physical and biological phenomena has long been the domain of computational ...
This work presents a geometric-deep-learning multi-mesh autoencoder framework for static and unstead...
In recent years, deep learning has opened countless research opportunities across many different dis...
The renewed interest from the scientific community in machine learning (ML) is opening many new area...
We present a novel physics-informed deep learning framework for solving steady-state incompressible ...
Optimization and uncertainty quantification have been playing an increasingly important role in comp...
A convolution neural network (CNN)-based approach for the construction of reduced order surrogate mo...
We propose the geometry-informed neural operator (GINO), a highly efficient approach to learning the...
Despite several advancements in experimental and computational resources, and despite progress in th...
A study to analyze the efficacy of two novel, state-of-the-art deep learning methods used in flow-fi...
In this thesis we explore machine and deep learning approaches that address keychallenges in high di...
Computational fluid dynamics (CFD) is the de-facto method for solving the Navier-Stokes equations, t...
The main objective of this thesis was to explore the capabilities of neural networks in terms of rep...
Determining the behavior of fluids is of interest in many fields. In this work, we focus on incompr...
The usage of neural networks (NNs) for flow reconstruction (FR) tasks from a limited number of senso...
The modeling of complex physical and biological phenomena has long been the domain of computational ...
This work presents a geometric-deep-learning multi-mesh autoencoder framework for static and unstead...
In recent years, deep learning has opened countless research opportunities across many different dis...
The renewed interest from the scientific community in machine learning (ML) is opening many new area...
We present a novel physics-informed deep learning framework for solving steady-state incompressible ...
Optimization and uncertainty quantification have been playing an increasingly important role in comp...
A convolution neural network (CNN)-based approach for the construction of reduced order surrogate mo...
We propose the geometry-informed neural operator (GINO), a highly efficient approach to learning the...
Despite several advancements in experimental and computational resources, and despite progress in th...