A physics-informed deep learning model, PBCNet, is proposed for predicting the relative binding affinity of ligands in order to improve guiding structure-based drug lead optimization.

In contrast, the author's machine learning algorithm is equipped with atmospheric physics equations that can produce more accurate results faster and with less data.

PNNL intern Luis Cedillo, an undergraduate at the University of Texas El Paso, presented a conference poster at SPIE Defense and Commercial Sensing 2024 in National Harbor, Maryland, about using the ...

Organic photovoltaics max out at 15%-20% efficiency. Lehigh University researchers are using physics-informed machine learning to improve this efficiency. Their findings suggest a machine learning ...

“Unlike traditional numerical models, our model employs an advanced physics-informed machine learning framework,” said author Feng Hu.

To address this, the research group used a "physics-informed" machine learning approach to infer these disorder characteristics indirectly.

Machine learning can get a boost from quantum physics. On certain types of machine learning tasks, quantum computers have an exponential advantage over standard computation, scientists report in ...

The Kennedy College of Science, Department of Physics & Applied Physics, invites you to attend a doctoral thesis defense by Abantika Ghosh on "Physics-Informed Machine Learning for Optical ...

Improving hurricane modeling with physics-informed machine learning Algorithm reconstructs wind fields quickly, accurately, and with less observational data Date: November 19, 2024 Source ...