The in-state rivalry between the Yellow Jackets and the Bulldogs usually heats up when Georgia Tech visits the University of ...

Researchers present a comprehensive review of frontier AI applications in computational structural analysis from 2020 to 2025, focusing on graph neural networks (GNNs), sequence-to-sequence (Seq2Seq) ...

Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...

Abstract: This paper introduces a Physics-Informed Koopman Neural Operator (PI-KNO) for augmented dynamics visual servoing of multirotors that integrates Koopman operator theory with neural networks.

How deep sea saturation divers survive crushing pressure, physics, chambers, and life at depth Posted: January 22, 2026 | Last updated: January 22, 2026 Offshore saturation divers routinely work at ...

Abstract: Deep learning models trained on finite data lack a complete understanding of the physical world. On the other hand, physics-informed neural networks (PINNs) are infused with such knowledge ...

Foams are everywhere: soap suds, shaving cream, whipped toppings and food emulsions like mayonnaise. For decades, scientists believed that foams behave like glass, their microscopic components trapped ...

Explore the advancements in minimal residual disease (MRD) assays, comparing tumor-informed and tumor-agnostic methods for enhanced cancer detection and treatment strategies. Minimal residual disease ...

One of the key steps in developing new materials is property identification, which has long relied on massive amounts of experimental data and expensive equipment, limiting research efficiency. A ...