Exciting news! @TDLebese_ presented at the Prognostics and Health Management (PHM) conference in Paris (May 31 - June 2, 2023) with their paper on "Unsupervised Representation Learning in Multivariate Time Series with Simulated Data." They won the Best Paper Award! 🎉👏 #PHM
The communication around the project is expanding!
We produce a #newsletter about the latest results and events twice a year. #Subscribe to make sure you receive these interesting updates!
https://t.co/kSxttP4l6B #machinelearning#ITN @MSCActions
PhD #Scholarship opportunity in "AI approaches and automatic uncertainty for robust, reliable, and trustful digital twin" at Strathclyde University
https://t.co/nRfnvu704r
UQLab V2.0, @ETH_en uncertainty quantification software, has been released on February 1st! It is now fully open source, i.e. available for free to anybody. Congrats to @SteMarelli for leading the dev team!
Check our new modules at https://t.co/OWKO5803mX
Congratulations to Maliki Moustapha and @SteMarelli for their recent paper in Structural Safety on active learning methods for reliability analysis!
More details here: https://t.co/F5uQaqweMV
Congratulations to Nora Lüthen and @SteMarelli for the comprehensive review and benchmark on sparse polynomial chaos expansions, just published by the SIAM/ASA Journal on Uncertainty Quantification.
https://t.co/18YNEO2B0v
Happy to announce the release of UQ[Py]Lab, a python, cloud-based version of UQLab, our software for uncertainty quantification.
You are invited to join the beta testing phase of this service by registering at the website: https://t.co/xkw1lK0itR
UQLab V.1.4 was released on February 1st!
Congratulations to the Dev Team led by @SteMarelli for the release. Among others, two brand new modules:
Active learning-based reliability analysis and the High-performance computing Dispatcher.
More details on https://t.co/QXeEQ51hOt