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"

Emergent weight morphologies in deep neural networks - Code

Pascal de Jong

Code used to produce the results for the manuscript "Emergent weight morphologies in deep neural networks".

Also available via: European Organization for Nuclear Research

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

hekeller

This repository contains the Juypter Notebooks and python files to reproduce the main results of the paper: Quantum neural networks for cloud cover parameterizations in climate models, Lorenzo et al. 2026

Development of a Convolutional Neural Network-Based Web Application for Automated Skin Disease Classification

Fatima Enehezei Usman-Hamza

Dermatological disorders remain a significant global healthcare challenge, affecting millions of individuals and contributing to increased disease burden, particularly when delayed or inaccurate diagnosis affects treatment outcomes. Although artificial intelligence has demonstrat…Read more

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

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

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