3 citations · 7 across the 8 of their papers we have counts for
8 papers
Fine-tuned Normalizing Flows for ALICE Zero Degree Calorimeter Fast Simulation
Emilia Majerz, Jacek Otwinowski, Witold Dzwinel +1
Simulating the ALICE Zero Degree Calorimeter (ZDC) neutron detector responses at the LHC is computationally expensive, requiring complex Monte Carlo chains. We develop a generative…
Inverse Autoregressive Flows for Zero Degree Calorimeter fast simulation
Emilia Majerz, Witold Dzwinel, Jacek Kitowski
Physics-based machine learning blends traditional science with modern data-driven techniques. Rather than relying exclusively on empirical data or predefined equations, this method…
Energy-Efficient Deep Learning Without Backpropagation: A Rigorous Hardware-Validated Benchmarking Study of Forward-Only Algorithms
Przemysław Spyra, Witold Dzwinel
Backpropagation (BP) powers modern deep learning, but it must hold every activation in memory until a global backward sweep consumes it, and that sweep locks each layer until the o…
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…
Quasi-optimal -finite element refinements towards singularities via deep neural network prediction
Tomasz Sluzalec, Rafal Grzeszczuk, Sergio Rojas +2
We show how to construct the deep neural network (DNN) expert to predict quasi-optimal -refinements for a given computational problem. The main idea is to train the DNN expert…
SuperNet -- An efficient method of neural networks ensembling
Ludwik Bukowski, Witold Dzwinel
The main flaw of neural network ensembling is that it is exceptionally demanding computationally, especially, if the individual sub-models are large neural networks, which must be…
Supermodeling of tumor dynamics with parallel isogeometric analysis solver
Maciej Paszynski, Leszek Siwik, Witold Dzwinel +1
Supermodeling is a modern, model-ensembling paradigm that integrates several self-synchronized imperfect sub-models by controlling a few meta-parameters to generate more accurate p…
2-D Embedding of Large and High-dimensional Data with Minimal Memory and Computational Time Requirements
Witold Dzwinel, Rafal Wcislo, Stan Matwin
In the advent of big data era, interactive visualization of large data sets consisting of M*10^5+ high-dimensional feature vectors of length N (N ~ 10^3+), is an indispensable tool…