approximating-a-kernel-of-truth

Approximating a kernel of truth

By using an approximate rather than explicit “kernel” function to extract relationships in very large data sets, KAUST researchers have been able to dramatically accelerate the speed of machine learning. The approach promises to greatly improve the speed of artificial intelligence (AI) in the era of big data. Read More Views: 0

helping-non-experts-create-mathematical-models-through-natural-selection

Helping non-experts create mathematical models through natural selection

Science and engineering applications such as control of high-precision motion systems or electrochemical processes are often built on mathematical models of dynamic systems. Ph.D. candidate Dhruv Khandelwal developed a framework that allows people without experience in data-driven modeling to fairly easily develop high-quality, optimized mathematical models of these dynamic systems. This is a vital tool…

data-driven-machine-learning-is-the-best-approach-for-advanced-battery-modelling

Data-driven machine learning is the best approach for advanced battery modelling

Demand for electrification of transport has emerged in recent years due to increasing concerns about global warming. The widespread adoption of electric vehicles will result in reduced harmful emissions and cleaner air, among other social and economic benefits. The battery industry is in need of software solutions for battery manufacturers to reduce fabrication and development…