5 papers
Collocation-based Robust Variational Physics-Informed Neural Networks (CRVPINN)
Marcin ÅoÅ, Tomasz SÅużalec, PaweÅ Maczuga +3
Physics-Informed Neural Networks (PINNs) have been successfully applied to solve Partial Differential Equations (PDEs). Their loss function is founded on a strong residual minimiza…
Augmenting MRI scan data with real-time predictions of glioblastoma brain tumor evolution using faster exponential time integrators
Magdalena Pabisz, Judit Muñoz-Matute, Maciej PaszyÅski
We present a MATLAB code for exponential integrators method simulating the glioblastoma tumor growth. It employs the Fisher-Kolmogorov diffusion-reaction tumor brain model with log…
Graph grammars and Physics Informed Neural Networks for simulating of pollution propagation on Spitzbergen
Maciej Sikora, Albert Oliver-Serra, Leszek Siwik +6
In this paper, we present two computational methods for performing simulations of pollution propagation described by advection-diffusion equations. The first method employs graph g…
Simulating the aftermath of Northern European Enclosure Dam (NEED) break and flooding of European coast
PaweÅ Maczuga, Marcin ÅoÅ, Eirik Valseth +5
The Northern European Enclosure Dam (NEED) is a hypothetical project to prevent flooding in European countries following the rising ocean level due to melting arctic glaciers. This…
Physics Informed Neural Network Code for 2D Transient Problems (PINN-2DT) Compatible with Google Colab
PaweÅ Maczuga, Maciej Sikora, Maciej SkoczeÅ +7
We present an open-source Physics Informed Neural Network environment for simulations of transient phenomena on two-dimensional rectangular domains, with the following features: (1…