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August 13, 2026
‘Working with AI Solves a Wide Range of Engineering Problems
Artificial intelligence is a working tool based on a balanced combination of algorithms and engineering. Experts and doctoral students from the HSE Moscow Institute of Electronics and Mathematics explain how AI technologies can improve an application, device, or system, and what engineering tasks are solved in the process.
August 12, 2026
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Whatever task Saraa Ali, Junior Research Fellow at the Laboratory of Methods for Big Data Analysis (LAMBDA) of the AI and Digital Science Institute (HSE Faculty of Computer Science), is working on, she thinks about how it can benefit people. She told the Young Scientists of HSE University project about her large family, diagnosing three-phase motors, and her dream of building a children’s home in her native country.
August 11, 2026
‘The Peak of Stupidity and ‘The Valley of Despair: HSE Economists Propose an Explanation for the Dunning–Kruger Effect
The Dunning–Kruger effect, which describes a sharp surge in self-confidence among beginners followed by an equally rapid decline as they gain experience, can be explained by the nature of the learning process and the acquisition of new knowledge. This conclusion was reached by Andrey Vorchik of the HSE Faculty of Economic Sciences together with independent researcher Murat Mamyshev. They developed a mathematical model of learning and demonstrated how subjective confidence is formed and changes as knowledge accumulates, as well as how teachers can reduce the ‘valley of despair’ experienced by learners.

 

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WAM, SWAN and WAVEWATCH III in the Finnish archipelago–the effect of spectral performance on bulk wave parameters

Journal of Operational Oceanography. 2020. Vol. 13. No. 1. P. 55–70.
Björkqvist J., Vähä-Piikkiö O., Alari V., Kuznetsova A., Tuomi L.

WAM, SWAN and WAVEWATCH III® were implemented to the Finnish archipelago with a 0.1 nmi grid. A comparison with coastal wave buoy observations showed that the models agreed on the significant wave height, with biases and root-mean-square-errors (RMSE) differing at most 0.06 m. In a general sense, WAM propagated most long wave energy into the archipelago, while SWAN generated the highest local waves. The performance of WAVEWATCH III was wind direction dependent. The model tendencies caused them to disagree on the peak period near the coast, with differences in mean values being up to 1.4 s. The large scatter (RMSE>2 s) inside the archipelago was mostly explained by the ill-defined nature of the parameter in more complex wave conditions. The mean period had less scatter (RMSE<1.5 s), but changes in the upper integration frequency from 0.6 Hz to 1 Hz affected the bias by roughly 1 s in all models. WAM and WAVEWATCH III underestimated the high-frequency wave energy for certain wind directions, possibly because of a too small friction velocity. A wind forcing taken every 3 h from a 7.4 km operational atmospheric model was found to be sufficient to force the high-resolution wave models.

Research target: Earth Sciences Computer Science
Language: English
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Keywords: validationвалидацияwave modelволновая модельwave periodbuoy measurementscoastalпериод волныприбрежнаяизмерение буем
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