Combinatorial optimization problems are often encountered in real-world applications, including logistics, scheduling and ...

Solving complex optimization problems is central to many modern technologies, from logistics and financial modeling to chip ...

Explore predictive modeling for compound prioritization, including in silico screening, toxicology models, and lead selection ...

This project implements state-of-the-art deep learning models for financial time series forecasting with a focus on uncertainty quantification. The system provides not just point predictions, but ...

Objectives Elective non-emergent surgical wait times have increased across countries such as Canada, straining operating room ...

Abstract: Assessing the failure of urban gas pipelines is crucial for identifying risk factors and preventing gas accidents that result in economic losses and casualties. Most previous studies on gas ...

Researchers have used machine learning to design nano-architected materials that have the strength of carbon steel but the lightness of Styrofoam. The team describes how they made nanomaterials with ...

Experimental variogram modelling is an essential process in geostatistics. The use of artificial intelligence (AI) is a new and advanced way of automating experimental variogram modelling. One part of ...