2 citations · 6 across the 10 of their papers we have counts for
11 papers
Cascading Blackout Severity Prediction with Statistically-Augmented Graph Neural Networks
Joe Gorka, Tim Hsu, Wenting Li +2
Higher variability in grid conditions, resulting from growing renewable penetration and increased incidence of extreme weather events, has increased the difficulty of screening for…
CMIP X-MOS: Improving Climate Models with Extreme Model Output Statistics
Vsevolod Morozov, Artem Galliamov, Aleksandr Lukashevich +2
Climate models are essential for assessing the impact of greenhouse gas emissions on our changing climate and the resulting increase in the frequency and severity of natural disast…
Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case Study
Valeriy Shevchenko, Daria Taniushkina, Aleksander Lukashevich +7
The United Nations has identified improving food security and reducing hunger as essential components of its sustainable development goals. As of 2021, approximately 828 million pe…
Accessing Convective Hazards Frequency Shift with Climate Change using Physics-Informed Machine Learning
Mikhail Mozikov, Ilya Makarov, Alexandr Bulkin +3
In this paper we discuss and address the challenges of predicting extreme atmospheric events like intense rainfall, hail, and strong winds. These events can cause significant damag…
Assessing the Risk of Permafrost Degradation with Physics-Informed Machine Learning
Polina Pilyugina, Timofey Chernikov, Alexey Zaytsev +6
Global warming accelerates permafrost degradation, impacting the reliability of critical infrastructure used by more than five million people daily. Furthermore, permafrost thaw pr…
GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow
Mile Mitrovic, Ognjen Kundacina, Aleksandr Lukashevich +4
The Gaussian Process (GP) based Chance-Constrained Optimal Power Flow (CC-OPF) is an open-source Python code developed for solving economic dispatch (ED) problem in modern power gr…