Code for Long-Time KdV Soliton Propagation Using Co-Moving Conservation-Regularized Physics-Informed Neural Networks

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

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

Quality of Service (QoS) Optimization in 5G/6G Networks Using Neural Networks

Charis E Shiny, S Annapurna, C Lakshana, Anusha Fakirappa Bogur, S Ramesh, G R Naik

Abstract: 5G is rolled out and next generation 6G networks are also being developed, ultra-low latency (URLL) communication as a standard is critical in supporting the plethora of applications, spanning autonomous vehicles, immersive extended reality experience, etc. However, tra…Read more

From Differential Equations to Deep Networks: A Unified Applied Mathematics and Computer Science Framework for Physics-Informed Computational Mechanics

Md.Nimur Rahman Durjoy

Here’s a line that’s been true for a while now but that we don’t talk about enough: the oldwalls between pure mathematical analysis, numerical computation, and mechanical modelingare quietly coming down, and modern scientific machine learning is basically the wreckingball. In thi…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

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