Li, HuiruYin, JianhuaDu, Xiaoping2023-05-052023-05-052022-01Li, H., Yin, J., & Du, X. (2022, January 3). Label Free Uncertainty Quantification. AIAA SCITECH 2022 Forum. AIAA SCITECH 2022 Forum, San Diego, CA & Virtual. https://doi.org/10.2514/6.2022-1097978-1-62410-631-6https://hdl.handle.net/1805/32827View Video Presentation: https://doi.org/10.2514/6.2022-1097.vid Uncertainty quantification (UQ) is essential in scientific computation since it can provide the estimate of the uncertainty in the model prediction. Intensive computation is required for UQ as it calls the deterministic simulation repeatedly. This study discusses a physics-based label-free deep learning UQ method that does not need predictions at training points or labels. It satisfies the physical equations from which labels could be generated without solving the equations during the training process. Then inexpensive surrogate models are built with respect to model inputs. The surrogate models are used for UQ with a much lower computational cost. Two examples demonstrate that the label-free method can efficiently produce probability distributions of model outputs for given distributions of random input variables.en-USPublisher PolicyUncertainty quantificationsurrogate modelsscientific computationConference proceedings