A research team from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has proposed a novel model optimization algorithm—External Calibration-Assisted Screening (ECA)— that ...

New reasoning models have something interesting and compelling called “chain of thought.” What that means, in a nutshell, is that the engine spits out a line of text attempting to tell the user what ...

Artificial intelligence (AI) is playing a huge role in heat rate optimization. In some cases, AI-driven models have analyzed operational data to recommend control settings that reduce heat rates by 1.

In this article, we present a nonlinear robust model predictive control (MPC) framework for general (state and input dependent) disturbances. This approach uses an online constructed tube in order to ...

“We need models that can adapt to the messiness of real life,” said Abolade. “That’s the only way we can turn numbers into meaningful action.” ...

Ensuring safe driving under real-time uncertainties remains a critical challenge in autonomous vehicle control. To address this issue for a collision avoidance task, this study proposes a robust model ...

The final predictions are the sum of the raw linear predictions and the residuals modeled by the Random Forest. Linear Boosting is a two stage learning process. Firstly, a linear model is trained on ...

HiGHS is a high performance serial and parallel solver for large scale sparse linear optimization problems of the form $$ \min \quad \dfrac {1} {2}x^TQx + c^Tx \qquad \textrm {s.t.}~ \quad L \leq Ax ...

A ribonuclease-targeting chimera (RiboTAC) is a heterobifunctional compound that binds to an RNA target and recruits a ribonuclease to cleave the bound RNA. This study investigates the impact of ...