Abstract Kolmogorov–Arnold Networks (KANs) were recently introduced as an alternative
representation model to MLP. Herein, we employ KANs to construct physics-informed …

While physics-informed neural networks (PINNs) have become a popular deep learning
framework for tackling forward and inverse problems governed by partial differential …

Engineering-scale problems generally can be described by partial differential equations
(PDEs) or ordinary differential equations (ODEs). Analytical, semi-analytical and numerical …

The utilization of neural operators in a digital twin model of an offshore floating structure
holds the potential for a significant shift in the prediction of structural responses and health …

Physics-informed neural networks (PINNs) have emerged as a significant endeavour in
recent years to utilize artificial intelligence technology for solving various partial differential …

We introduce a method to convert Physics-Informed Neural Networks (PINNs), commonly
used in scientific machine learning, to Spiking Neural Networks (SNNs), which are expected …

This paper presents an ansatz-informed approach to modeling the dynamics of blunt-body
entry vehicles by combining physics-based modeling with machine-learning techniques …

Cerebrospinal fluid (CSF) flow is crucial for clearing metabolic waste from the brain, a
process whose dysregulation is linked to neurodegenerative diseases like Alzheimer's …

In this review, we survey the latest approaches and techniques developed to overcome the
spectral bias towards low frequency of deep neural network learning methods in learning …

We propose a novel framework for uncertainty quantification via information bottleneck (IB-
UQ) for scientific machine learning tasks, including deep neural network (DNN) regression …