id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
0v83805292k	Yinhao Zhu	Data-Driven and Physics-Constrained Deep Learning for Surrogate Modeling and Uncertainty Quantification of Physical Systems	2019		.txt	text/plain	218	8	24	Surrogate modeling is computationally attractive for problems that require repetitive yet expensive simulations of PDEs, such as uncertainty propagation, deterministic design or inverse problems, where the main challenges are curse of dimensionality, data efficiency, uncertainty quantification and generalization, especially for problems with high dimensional input. We further explore how to incorporate the governing equations of the physical models into the loss/likelihood functions of the physics-constrained surrogates to completely avoid any simulation (or labeled data) while achieving similar accuracy with the date-driven surrogates.	cache/0v83805292k.txt	txt/0v83805292k.txt