In the current era of big data, the volume of information continues to grow at an unprecedented rate, giving rise to the crucial need for efficient ...

The era of predictive modeling enhanced with machine learning and artificial intelligence (AI) to aid clinical ...

Researchers have demonstrated a new technique that allows "self-driving laboratories" to collect at least 10 times more data ...

A new leap in lab automation is shaking up how scientists discover materials. By switching from slow, traditional methods to real-time, dynamic chemical experiments, researchers have created a ...

Using an advanced Monte Carlo method, Caltech researchers found a way to tame the infinite complexity of Feynman diagrams and ...

Semiconductor processing is notoriously challenging. It is one of the most intricate feats of modern engineering due to the ...

In a first, Australian scientists turned to quantum machine learning to model semiconductor design, outperforming classical ...

In this article, we focus on the scalable distributed data-driven state estimation problem using Gaussian processes (GPs). The framework includes two parts: 1) the data-driven training approach and 2) ...

Therefore, this study proposes a system-level calibration optimal path planning method based on an improved Dijkstra’s algorithm. First, the system-level calibration optimal path planning problem was ...

To make a truly realistic fake picture of a galaxy, you can model exactly how light particles travel through the atmosphere and telescope to reach its sensor.

In The Fourth Paradigm: Data-Intensive Scientific Discovery, the collection of essays expands on the vision of pioneering computer scientist Jim Gray for a new, fourth paradigm of discovery based on ...

In a nutshell Most commercial fitness trackers provide inaccurate calorie burn estimates for people with obesity, due to differences in body shape, movement, and gait that these devices weren’t ...