This paper tries to give a gentle introduction to deep learning in medical image processing,
proceeding from theoretical foundations to applications. We first discuss general reasons for …

While deep learning has shown tremendous success in a wide range of domains, it remains
a grand challenge to incorporate physical principles in a systematic manner to the design …

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 …

Finding accurate solutions to partial differential equations (PDEs) is a crucial task in all
scientific and engineering disciplines. It has recently been shown that machine learning …

This paper presents a novel generative model to synthesize fluid simulations from a set of
reduced parameters. A convolutional neural network is trained on a collection of discrete …

We propose a temporally coherent generative model addressing the super-resolution
problem for fluid flows. Our work represents a first approach to synthesize four-dimensional …

The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …

Transformer has shown state-of-the-art performance on various applications and has
recently emerged as a promising tool for surrogate modeling of partial differential equations …

We propose a method for the data‐driven inference of temporal evolutions of physical
functions with deep learning. More specifically, we target fluid flow problems, and we …

We present a novel method to generate accurate and realistic clothing deformation from real
data capture. Previous methods for realistic cloth modeling mainly rely on intensive …