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pro vyhledávání: '"Mollaali, Amirhossein"'
In this paper, we adopt conformal prediction, a distribution-free uncertainty quantification (UQ) framework, to obtain confidence prediction intervals with coverage guarantees for Deep Operator Network (DeepONet) regression. Initially, we enhance the
Externí odkaz:
http://arxiv.org/abs/2402.15406
Autor:
Mollaali, Amirhossein, Sahin, Izzet, Raza, Iqrar, Moya, Christian, Paniagua, Guillermo, Lin, Guang
In the pursuit of accurate experimental and computational data while minimizing effort, there is a constant need for high-fidelity results. However, achieving such results often requires significant computational resources. To address this challenge,
Externí odkaz:
http://arxiv.org/abs/2311.03639
This paper designs surrogate models with uncertainty quantification capabilities to improve the thermal performance of rib-turbulated internal cooling channels effectively. To construct the surrogate, we use the deep operator network (DeepONet) frame
Externí odkaz:
http://arxiv.org/abs/2306.00810
Publikováno v:
In International Journal of Heat and Mass Transfer February 2024 219