Frontiers

Hybrid Hydroinformatics: Integrating Machine Learning and Process-Based Modeling for Climate-Resilient Water Systems

The hydrologic system is subjected unprecedented stresses and increasing demands driven by climate variabilities, landuse changes, groundwater ...
Nature

Hybrid machine learning and Gaussian process for antenna parameter estimation

This paper presents a hybrid method of combining the Random Forest (RF) algorithm in machine learning (ML) and the Gaussian process (GP) to design microstrip patch antennas at any frequency from 0.6 ...
Forbes

Hybrid Quantum: Classical Architectures For Machine Learning

Integrating quantum computing into AI doesn’t require rebuilding neural networks from scratch. Instead, I’ve found the most effective approach is to introduce a small quantum block—essentially a ...
Nature

Design of a hybrid quantum machine learning architecture and analysis of quantum noise effects

Quantum Machine Learning (QML) is emerging as a promising technology for tackling complex computational challenges, although its practical implementation faces significant obstacles owed to the ...
Phys.org

Automating microfluidic chip design: Hybrid approach combines machine learning with fluid mechanics

Researchers led by Assoc. Prof. Dr. Savaş Taşoğlu from the Department of Mechanical Engineering at Koç University have developed a new, open-access and machine learning-assisted design tool aimed at ...
Frontiers

Early Warning Systems for Coastal and Nearshore Infrastructure: Integrating Forecasting, Data Assimilation, and Machine Learning

Coastal and nearshore zones face growing pressure from storms, flooding, erosion, and sea-level rise, which threaten civil infrastructure such as ports, ...