699 citations · 2.7k across the 97 of their papers we have counts for
6 papers · 1 filter
Evolutionary Two-Stage Hyperparameter Optimization Strategies for Physics-Informed Neural Networks
Fedor Buzaev, Dmitry Efremenko, Egor Bugaev +4
Physics-Informed Neural Networks (PINNs) solve Partial Differential Equations (PDEs) by embedding physical laws into neural network training. However, their performance suffers fro…
Correcting Sensor-Induced Distribution Drift with Wasserstein Adversarial Learning
Saraa Ali, Vladimir Bocharnikov, Fedor Ratnikov +3
The quality of recorded data depends on the stability of the sensor system that acquires it. Sensor motion and aging can degrade the performance and stability of downstream data-dr…
Physics-Constrained Neural Networks for Improved Short-Term Weather Forecasting: A Case Study over the South Pacific
Egor Bugaev, Fedor Buzaev, Dmitry Efremenko +2
This study introduces enhancements to physics-constrained neural networks (PCNNs) that improve the accuracy and stability of hybrid short-term weather forecasting models. Building…
RL-ABC: Reinforcement Learning for Accelerator Beamline Control
Anwar Ibrahim, Fedor Ratnikov, Maxim Kaledin +2
Particle accelerator beamline optimization is a high-dimensional control problem traditionally requiring significant expert intervention. We present RLABC (Reinforcement Learning f…
Approach to Finding a Robust Deep Learning Model
Alexey Boldyrev, Fedor Ratnikov, Andrey Shevelev
The rapid development of machine learning (ML) and artificial intelligence (AI) applications requires the training of large numbers of models. This growing demand highlights the im…
Calibrating for the Future:Enhancing Calorimeter Longevity with Deep Learning
S. Ali, A. S. Ryzhikov, D. A. Derkach +2
In the realm of high-energy physics, the longevity of calorimeters is paramount. Our research introduces a deep learning strategy to refine the calibration process of calorimeters…