Computer graphics, 3D computer vision and robotics communities have produced multiple
approaches to representing 3D geometry for rendering and reconstruction. These provide …

This study investigates the accuracy of deep learning models for the inference of Reynolds-
averaged Navier–Stokes (RANS) solutions. This study focuses on a modernized U-net …

Abstract Geometric Deep Learning has recently made striking progress with the advent of
continuous Deep Implicit Fields. They allow for detailed modeling of watertight surfaces of …

Abstract Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning
toolbox and have led to many breakthroughs in Artificial Intelligence. So far, these neural …

Abstract Unsigned Distance Fields (UDFs) can be used to represent non-watertight surfaces.
However, current approaches to converting them into explicit meshes tend to either be …

Implicit Neural Representations (INRs) have emerged in the last few years as a powerful tool
to encode continuously a variety of different signals like images, videos, audio and 3D …

Constructing fast numerical solvers for partial differential equations (PDEs) is crucial for
many scientific disciplines. A leading technique for solving large-scale PDEs is using …

Aircraft design requires a multitude of aerodynamic data and providing this solely based on
high-quality methods such as computational fluid dynamics is prohibitive from a cost and …

We present a parametric approach for heat exchanger shape optimization utilizing Deep
Reinforcement Learning (Deep RL) and Boundary Representation (BREP). In this study, we …

In the field of aerodynamics, shape optimization is a very critical step. However,
Computational Fluid Dynamics (CFD) simulation tools are computationally expensive to be …