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

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

Software and reproduction data for Algorithmic first-principles reaction discovery uncovers overlooked cubane transformations

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

Reproduction data for the associated manuscript This record contains the software, input files, and precomputed data required to reproduce the calculations reported in Algorithmic first-principles reaction discovery uncovers overlooked cubane transformations The archive includes…Read more

Also available via: European Organization for Nuclear Research

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

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

Dataset for Density functional and neural-network potential simulations of Ag migration in disordered GeS2 electrolytes

Jaakko Akola, Sondre Dahl, Adam Götz, R. O. Jones

Bayesian-Optimized Physics-Informed Neural Networks for the FitzHugh-Nagumo Model

Bogdan Miličević, N Filipovic

Physics-Informed Neural Networks (PINNs) offer a promising bridge between deep learning and biophysical modeling by embedding differential equations directly into the learning process. This paper explores an automated framework using Bayesian Optimization (BO) and PINNs in order…Read more