Výsledky vyhledávání - "Tsapetis, Dimitrios"

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On Active Learning for Gaussian Process-based Global Sensitivity Analysis

Autor: Chauhan, Mohit, Ojeda-Tuz, Mariel, Catarelli, Ryan, Gurley, Kurtis, Tsapetis, Dimitrios, Shields, Michael D.

This paper explores the application of active learning strategies to adaptively learn Sobol indices for global sensitivity analysis. We demonstrate that active learning for Sobol indices poses unique challenges due to the definition of the Sobol inde

Externí odkaz: http://arxiv.org/abs/2308.14220

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Learning thermodynamically constrained equations of state with uncertainty

Autor: Sharma, Himanshu, Gaffney, Jim A., Tsapetis, Dimitrios, Shields, Michael D.

Publikováno v: APL Machine Learning 2.1 (2024)

Numerical simulations of high energy-density experiments require equation of state (EOS) models that relate a material's thermodynamic state variables -- specifically pressure, volume/density, energy, and temperature. EOS models are typically constru

Externí odkaz: http://arxiv.org/abs/2306.17004

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UQpy v4.1: Uncertainty Quantification with Python

Autor: Tsapetis, Dimitrios, Shields, Michael D., Giovanis, Dimitris G., Olivier, Audrey, Novak, Lukas, Chakroborty, Promit, Sharma, Himanshu, Chauhan, Mohit, Kontolati, Katiana, Vandanapu, Lohit, Loukrezis, Dimitrios, Gardner, Michael

This paper presents the latest improvements introduced in Version 4 of the UQpy, Uncertainty Quantification with Python, library. In the latest version, the code was restructured to conform with the latest Python coding conventions, refactored to sim

Externí odkaz: http://arxiv.org/abs/2305.09572

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