Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

The incorporation of physical information in machine learning frameworks is opening and
transforming many application domains. Here the learning process is augmented through …

We present a novel physics-informed deep learning framework for solving steady-state
incompressible flow on multiple sets of irregular geometries by incorporating two main …

Understanding and accurately predicting complex three-dimensional (3D) temperature
distributions are critical in diverse domains, including climate science and industrial process …

Physics-Informed Machine Learning (PIML) is an emerging computing paradigm that offers a
new approach to tackle multiphysics modeling problems prevalent in the field of chemical …

Glacio-hydrological modeling is a key task for assessing the influence of snow and glaciers
on water resources, essential for water resources management. The present study aims to …

In many cutting-edge applications, high-fidelity computational models prove to be too slow
for practical use and are therefore replaced by much faster surrogate models. Recently …

Temperature field prediction is of great importance in the thermal design of systems
engineering, and building a surrogate model is an effective method for the task. Ensuring a …

We propose a novel finite element-based physics-informed operator learning framework that
allows for predicting spatiotemporal dynamics governed by partial differential equations …

Structural failures are often caused by catastrophic events such as earthquakes and winds.
As a result, it is crucial to predict dynamic stress distributions during highly disruptive events …