Partial differential equations (PDEs) are among the most universal and parsimonious
descriptions of natural physical laws, capturing a rich variety of phenomenology and …

This paper explores the recent advancements in enhancing Computational Fluid Dynamics
(CFD) tasks through Machine Learning (ML) techniques. We begin by introducing …

Pre-trained machine learning (ML) models have shown great performance for awide range
of applications, in particular in natural language processing (NLP) and computer vision (CV) …

Operator learning aims to discover properties of an underlying dynamical system or partial
differential equation (PDE) from data. Here, we present a step-by-step guide to operator …

Fast and accurate predictions of turbulent flows are of great importance in the science and
engineering field. In this paper, we investigate the implicit U-Net enhanced Fourier neural …

Can we build a single large model for a wide range of PDE-related scientific learning tasks?
Can this model generalize to new PDEs without any fine-tuning? In-context operator …

Learning operators with deep neural networks is an emerging paradigm for scientific
computing. Deep Operator Network (DeepONet) is a modular operator learning framework …

It is challenging to obtain the full-field temperature profile during autoclave processes to
control the temperature uniformity and minimize the residual stress and distortion of cured …

This research introduces an accelerated training approach for Vanilla Physics-Informed
Neural Networks (PINNs) that addresses three factors affecting the loss function: the initial …

With their development, machine learning models can be used instead of computational
fluid dynamics simulations to predict flow fields in aerodynamic optimization. However, it is …