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

Unsupervised learning with functional data is an emerging paradigm of machine learning
research with applications to computer vision, climate modeling and physical systems. A …

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

Multi-task learning through composite loss functions is fundamental to modern deep
learning, yet optimizing competing objectives remains challenging. We present new …

The study found that it is difficult to capture the solutions at the shock wave and discontinuity
surfaces when solving Euler equations using physics informed neural network. Thus, this …

This paper advances the use of physics-informed neural networks (PINNs) architectures to
address moving interface problems via the level set method. Originally developed for other …

In this report we summarize the work conducted by PI Perdikaris and his group under this
Early Career project DE‐SC0019116 during the period of 09/01/2018‐08/31/2023. The …

The advancement of scientific machine learning (ML) techniques has led to the development
of methods for approximating solutions to nonlinear partial differential equations (PDE) with …

Computer simulations based on partial differential equations (PDEs) describing physical
phenomena, are widely used for analyzing the performance of high voltage insulation. Such …

The development of efficient high-voltage equipment is imperative for minimizing
greenhouse gas emissions and saving costs within the energy system. Effective insulation …