EyringMLClimateGroup/pastori26MLEarth_QNN-cloudcover: Quantum neural networks for cloud cover parameterizations in climate models

Using Quantum Neural Networks for Efficient Quantum Portfolio Optimization

Atharva Indulkar

Current quantum portfolio optimization pipelines rely on Random Matrix Theory (RMT) forcorrelation matrix cleaning, a static linear method that cannot adapt when market regimesshift. Meanwhile, D-Wave's own operational audit shows the quantum processor runs for just0.68% of total…Read more

Data and Code to reproduce results in paper "A Systematic Literature Review on Graph-Based Models in Credit Risk Assessment"

Lennart John Baals, Yiting Liu, Joerg Osterrieder, Branka Hadji Misheva

Data and Code to reproduce results in paper "A Systematic Literature Review on Graph-Based Models in Credit Risk Assessment" This repository contains the necessary codes to reproduce results in the paper: Baals, L. J., Liu, Y., Osterrieder, J., & Hadji-Misheva, B. (2025). A Syste…Read more

Also available via: European Organization for Nuclear Research

Electrical resistivity tomography surveys, trained physics-informed neural network models and code for amortized ERT inversion along Route Regionale 707, Moroccan Middle Atlas

Rajae Ajana

This deposit contains the field data, synthetic training datasets, trained network weights and analysis code supporting the article "Physics-informed neural network inversion of electrical resistivity tomography data: amortized optimization with field validation in the Moroccan M…Read more

Hybrid Convolutional Neural Network, Long Short-Term Memory network Model for Fault Detection in Nigerian Oil and Gas Pipeline Infrastructure

Gilbert Ugwuanyi, Akpado Kenneth Aghaegbunam

Nigeria's oil and gas pipeline network spanning over 5,000 km of trunk lines and more than 3,000 km of flow lines loses an estimated one billion US dollars annually to pipeline failures, environmental incidents, and non-productive time. The dominant monitoring approach in operati…Read more

Trained neural network potential models for Algorithmic first-principles reaction discovery uncovers overlooked cubane transformations

Wataru Matsuoka, Taihei Oki, Kosaku Tanaka, Ren Yamada, Ruben Staub, Alexandre Varnek, et al.

This dataset contains the neural network potential (NNP) models used in the associated manuscript, including standalone executables and the Python interface for Au-, Ag-, and Cu-catalyzed systems. The models can be used together with the VLA-PRO package to reproduce the calculati…Read more

Also available via: European Organization for Nuclear Research

Code and dataset for manuscript "A Heteroscedastic Neural Network-based Turbulent Heat Flux Parameterization and Its Applications to an Ocean Modeling of the Tropical Pacific"

Zhou, Lu

This project includes the model code and observed heat flux data involved in the manuscript "A Heteroscedastic Neural Network-based Turbulent Heat Flux Parameterization and Its Applications to an Ocean Modeling of the Tropical Pacific".

Also available via: European Organization for Nuclear Research