In recent years, a plethora of methods combining neural networks and partial differential
equations have been developed. A widely known example are physics-informed neural …

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

In this paper, we introduce a new approach based on distance fields to exactly impose
boundary conditions in physics-informed deep neural networks. The challenges in satisfying …

Most engineering domains abound with models derived from first principles that have
beenproven to be effective for decades. These models are not only a valuable source of …

Experimental solid mechanics is at a pivotal point where machine learning (ML) approaches
are rapidly proliferating into the discovery process due to significant advances in data …

We characterize and remedy a failure mode that may arise from multi-scale dynamics with
scale imbalances during training of deep neural networks, such as physics informed neural …

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 …

While significant progress has been made on Physics-Informed Neural Networks (PINNs), a
comprehensive comparison of these methods across a wide range of Partial Differential …

This paper addresses how physical knowledge can improve machine learning in process
control. A data-driven tracking control framework using physics-informed neural networks …

In this paper, we present a novel methodology for automatic adaptive weighting of Bayesian
Physics-Informed Neural Networks (BPINNs), and we demonstrate that this makes it …