8 citations · 13 across the 5 of their papers we have counts for
5 papers
Data-Driven Estimation of Failure Probabilities in Correlated Structure-Preserving Stochastic Power System Models
Hongli Zhao, Tyler E. Maltba, D. Adrian Maldonado +2
We propose a data-driven approach for propagating uncertainty in stochastic power grid simulations and apply it to the estimation of transmission line failure probabilities. A redu…
Data-driven Closures & Assimilation for Stiff Multiscale Random Dynamics
Tyler E. Maltba, Hongli Zhao, D. Adrian Maldonado
We introduce a data-driven and physics-informed framework for propagating uncertainty in stiff, multiscale random ordinary differential equations (RODEs) driven by correlated (colo…
Tensorizing flows: a tool for variational inference
Yuehaw Khoo, Michael Lindsey, Hongli Zhao
Fueled by the expressive power of deep neural networks, normalizing flows have achieved spectacular success in generative modeling, or learning to draw new samples from a distribut…
High-dimensional density estimation with tensorizing flow
Yinuo Ren, Hongli Zhao, Yuehaw Khoo +1
We propose the tensorizing flow method for estimating high-dimensional probability density functions from the observed data. The method is based on tensor-train and flow-based gene…
Autonomous learning of nonlocal stochastic neuron dynamics
Tyler E. Maltba, Hongli Zhao, Daniel M. Tartakovsky
Neuronal dynamics is driven by externally imposed or internally generated random excitations/noise, and is often described by systems of random or stochastic ordinary differential…