Over the last decade, deep learning (DL), a branch of machine learning, has experienced
rapid progress. Powerful tools for tasks that have been traditionally complex to automate …

A new technological passage has emerged in the pharmaceutical field, concerning the
management, application, and transfer of knowledge from humans to machines, as well as …

We propose a customized convolutional neural network based autoencoder called a
hierarchical autoencoder, which allows us to extract nonlinear autoencoder modes of flow …

Transformers are widely used in natural language processing due to their ability to model
longer-term dependencies in text. Although these models achieve state-of-the-art …

Dynamical models underpin our ability to understand and predict the behaviour of natural
systems. Whether dynamical models are developed from first-principles derivations or from …

Graph-based next-step prediction models have recently been very successful in modeling
complex high-dimensional physical systems on irregular meshes. However, due to their …

Supervised learning in function spaces is an emerging area of machine learning research
with applications to the prediction of complex physical systems such as fluid flows, solid …

Variational autoencoder architectures have the potential to develop reduced-order models
for chaotic fluid flows. We propose a method for learning compact and near-orthogonal …

The complex flow modeling based on machine learning is becoming a promising way to
describe multiphase fluid systems. This work demonstrates how a physics-informed neural …

Traditional data-driven deep learning models often struggle with high training costs, error
accumulation, and poor generalizability in complex physical processes. Physics-informed …