Abstract: Portfolio optimization that allows the borrowed money from a loan to be invested in risk assets is formulated as a data-driven optimization problem called POL_P. Then, in order to reflect ...

Leaders run the risk of losing their strategic edge by blindly pushing AI for the sake of AI. Companies can no longer win the ...

Utilize AI to analyze application runtime data (e.g., rendering time, communication latency), obtain optimization suggestions (such as reducing component re-rendering, reusing hardware connections), ...

Amazon, Microsoft, and IBM are all racing to build a quantum computer. But the technology's feasibility is as hazy as its physics.

One of those stocks appears particularly interesting at the moment, with Wall Street analysts' price targets suggesting ...

AI doesn’t care if your site or product looks cool. The internet is traditionally a very visual place, and as such, many ...

Solving optimization problems is challenging for existing digital computers and even for future quantum hardware. The practical importance of diverse problems, from healthcare to financial ...

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...

1 School of Mathematics and Statistics, Fuzhou University, Fuzhou, China. 2 College of Computer and Data Science, Fuzhou University, Fuzhou, China. In this paper, we use Physics-Informed Neural ...

Change is the only constant in today’s rapidly evolving digital marketing landscape. Keeping up with the latest innovations isn’t just a choice – it’s a necessity for survival. Generative engine ...

The paper presents a topology optimization methodology for 2D elastodynamic problems using the boundary element method (BEM). The topological derivative is derived based on the variation method and ...