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Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning
Han Gao, Jian-Xun Wang, Matthew J. Zahr
Projection-based model reduction has become a popular approach to reduce the cost associated with integrating large-scale dynamical systems so they can be used in many-query settin…
Improving the Convergence of the Iterative Ensemble Kalman Filter by Resampling
Jiacheng Wu, Jian-Xun Wang, Shawn C. Shadden
The iterative ensemble Kalman filter (IEnKF) is widely used in inverse problems to estimate system parameters from limited observations. However, the IEnKF, when applied to nonline…
A Bi-fidelity Surrogate Modeling Approach for Uncertainty Propagation in Three-Dimensional Hemodynamic Simulations
Han Gao, Xueyu Zhu, Jian-Xun Wang
Image-based computational fluid dynamics (CFD) modeling enables derivation of hemodynamic information, which has become a paradigm in cardiovascular research and healthcare. Noneth…
Surrogate Modeling for Fluid Flows Based on Physics-Constrained Deep Learning Without Simulation Data
Luning Sun, Han Gao, Shaowu Pan +1
Numerical simulations on fluid dynamics problems primarily rely on spatially or/and temporally discretization of the governing equation into the finite-dimensional algebraic system…