Machine learning helps improve accuracy and efficiency of small-molecule calculations Microsoft researchers used deep learning to create new DFT model by Sam Lemonick, special to C&EN June 20, 2025 ...

This article introduces a probabilistic machine learning framework for the uncertainty quantification (UQ) of electronic circuits based on the Gaussian process regression (GPR). As opposed to ...

Electronic health records (EHR)-based machine learning (ML) approach to predict risk of progression to metastatic melanoma after initial diagnosis.. If you have the appropriate software installed, you ...

Machine Learning (ML) is extensively being used in order to tackle the problem of radio propagation. Besides the models that rely on tabular data, Deep Learning (DL)-based image-driven models have ...

This work demonstrates a new approach to atmospheric inverse modeling of CO2 emissions using Gaussian Process machine learning and modern probabilistic programming languages.

Like p-bits, g-bits serve as a fundamental building block for probabilistic computing, enabling optimization and machine learning with continuous variables.

In a breakthrough approach, a research team from the Technical University of Munich introduced a refined method that integrates simulator control signals into the flow-based generative modeling ...

The electric submersible pump (ESP) is widely used in oil extraction processes and is recognized for its effectiveness as an artificial lift technique in the petroleum sector. Developing and improving ...

Concept-based learning (CBL) in machine learning emphasizes using high-level concepts from raw features for predictions, enhancing model interpretability and efficiency. A prominent type, the ...

This study applies state-of-the-art machine learning (ML) techniques to forecast IMF-supported programs, analyzes the ML prediction results relative to traditional econometric approaches, explores non ...