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 to model electrophysiological processes. The FitzHugh-Nagumo (FHN) model is used as a fundamental system in excitable media research to test this approach. Our study uses BO to automatically tune the structural hyperparameters of the network, specifically the number of layers and neurons. This demonstrates the potential of BO-PINNs to simplify the model selection process for time-dependent dynamics. This paper was developed within the framework of the STRATIFYHF project.
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…
Physics-informed neural networks (PINNs) are promoted as a general differential-equation solver, but the accuracy actually achieved varies by orders of magnitude across problem types, and that variation is rarely laid out in one place. This report is a method atlas: a runnable ca…
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…
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…
Google Colab and Python code for generating an ETDRK4 numerical reference, training matched laboratory-frame and co-moving-frame physics-informed neural networks, applying PDE-dominant and conservation-aware optimization stages, computing diagnostics, and reproducing the main and…
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…