1 citations · 2 across the 7 of their papers we have counts for
7 papers
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
Paweł Maczuga, Marcin Łoś, 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…
Augmenting MRI scan data with real-time predictions of glioblastoma brain tumor evolution using faster exponential time integrators
Magdalena Pabisz, Judit Muñoz-Matute, Maciej Paszyński
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…
Collocation-based Robust Variational Physics-Informed Neural Networks (CRVPINN)
Marcin Łoś, Tomasz Służalec, Paweł 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…
Physics Informed Neural Network Code for 2D Transient Problems (PINN-2DT) Compatible with Google Colab
Paweł Maczuga, Maciej Sikora, Maciej Skoczeń +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…
Solver algorithm for stabilized space-time formulation of advection-dominated diffusion problem
Marcin Łoś, Paulina Sepulveda-Salas, Maciej Paszyński
This article shows how to develop an efficient solver for a stabilized numerical space-time formulation of the advection-dominated diffusion transient equation. At the discrete spa…
Deep neural networks for smooth approximation of physics with higher order and continuity B-spline base functions
Kamil Doległo, Anna Paszyńska, Maciej Paszyński +1
This paper deals with the following important research question. Traditionally, the neural network employs non-linear activation functions concatenated with linear operators to app…