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

The spatio-temporal evolution of wall-bounded turbulence is characterized by high nonlinearity, multi-scale dynamics, and ...

Abstract: We propose the Physics-Informed Neural Network-driven Sparse Field Discretization method (PINN-SFD), a novel self-supervised, physics-informed deep learning approach for addressing the ...

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.

This repository contains the source code for the paper "Space Correlation Constrained Physics Informed Neural Network for Seismic Tomography", accepted by JGR: Machine Learning and Computation on ...

With the exponential growth in demand for high-speed data transmission, the 5G system infrastructure, despite its impressive peak data rate of 10 gigabits per second, is increasingly inadequate for ...

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 ...

Doug Bonderud is an award-winning writer capable of bridging the gap between complex and conversational across technology, innovation and the human condition. As artificial intelligence becomes ...

The knowledge-informed deep learning (KIDL) paradigm, with the blue section representing the LLM workflow (teacher demonstration), the orange section representing the distillation pipeline of KIDL ...