Recent advances of data-driven machine learning have revolutionized fields like computer
vision, reinforcement learning, and many scientific and engineering domains. In many real …

Physics-informed neural networks (PINNs) have been popularized as a deep learning
framework that can seamlessly synthesize observational data and partial differential …

Physics-informed neural networks (PINNs) represent a significant advancement at the
intersection of machine learning and physical sciences, offering a powerful framework for …

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) …

Transfer learning enables the transfer of knowledge gained while learning to perform one
task (source) to a related but different task (target), hence addressing the expense of data …

Vascular flow modelling can improve our understanding of vascular pathologies and aid in
developing safe and effective medical devices. Vascular flow models typically involve …

Conventional continuum models for ion transport across polyamide membranes require
solving partial differential equations (PDEs). These models typically introduce a host of …

This paper introduces the paradigm of “in-context operator learning” and the corresponding
model “In-Context Operator Networks” to simultaneously learn operators from the prompted …

We introduce multi-head neural networks (MH-NNs) to physics-informed machine learning,
which is a type of neural networks (NNs) with all nonlinear hidden layers as the body and …

Physics‐informed neural networks (PINNs) are a new tool for solving boundary value
problems by defining loss functions of neural networks based on governing equations …