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Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non-Gaussian hydraulic conductivities
Shaoxing Mo, Nicholas Zabaras, Xiaoqing Shi +1
Inverse modeling for the estimation of non-Gaussian hydraulic conductivity fields in subsurface flow and solute transport models remains a challenging problem. This is mainly due t…
Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks
Nicholas Geneva, Nicholas Zabaras
In recent years, deep learning has proven to be a viable methodology for surrogate modeling and uncertainty quantification for a vast number of physical systems. However, in their…
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data
Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis +1
Surrogate modeling and uncertainty quantification tasks for PDE systems are most often considered as supervised learning problems where input and output data pairs are used for tra…