Phys.org

Ultra‑robust machine‑learning models run stable molecular simulations at extreme temperatures

Researchers at The University of Manchester have created a physics‑informed machine‑learning model that can run molecular simulations for unprecedented lengths of time, even at temperatures as high as ...
Nature

Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods

Precise estimation of rock petrophysical parameters are seriously important for the reliable computation of hydrocarbon in place in the underground formations. Therefore, accurately estimation rock ...
Nature

Robust evaluation of classical and quantum machine learning under noise, imbalance, feature reduction and explainability

Quantum machine learning (QML) has emerged as a promising paradigm for solving complex classification problems by leveraging the computational advantages of quantum systems. While most traditional ...

Machine learning rediscovers equations governing ocean biogeochemistry

Climate and ocean models use a series of equations to represent complex natural processes. However, the equations used in ...
EurekAlert!

Scientists develop ultra‑robust machine‑learning models capable of stable molecular simulations at extreme temperatures

The idea is there is the red hot glow to show the 'extreme' temperatures of our simulations, and some molecules with blurred motion effects to show that it is a simulation. The study, published in ...