25 citations · 58 across the 14 of their papers we have counts for
15 papers
Learning the Evolution of Correlated Stochastic Power System Dynamics
Tyler E. Maltba, Vishwas Rao, Daniel Adrian Maldonado
A machine learning technique is proposed for quantifying uncertainty in power system dynamics with spatiotemporally correlated stochastic forcing. We learn one-dimensional linear p…
Order-Disorder Transitions in (CaSr)RhSn
Puspa Upreti, Matthew Krogstad, Charlotte Haley +6
The classification of structural phase transitions as displacive or order-disorder in character is usually based on spectroscopic data above the transition. We use single crystal x…
CAMERA: A Method for Cost-aware, Adaptive, Multifidelity, Efficient Reliability Analysis
S. Ashwin Renganathan, Vishwas Rao, Ionel M. Navon
Estimating probability of failure in aerospace systems is a critical requirement for flight certification and qualification. Failure probability estimation involves resolving tails…
Efficient high-dimensional variational data assimilation with machine-learned reduced-order models
Romit Maulik, Vishwas Rao, Jiali Wang +6
Data assimilation (DA) in the geophysical sciences remains the cornerstone of robust forecasts from numerical models. Indeed, DA plays a crucial role in the quality of numerical we…
Trust-region approximation of extreme trajectories in power system dynamics
Daniel Adrian Maldonado, Emil Constantinescu, Hong Zhang +2
In this work we present a novel technique, based on a trust-region optimization algorithm and second-order trajectory sensitivities, to compute the extreme trajectories of power sy…
Optimization under rare chance constraints
Shanyin Tong, Anirudh Subramanyam, Vishwas Rao
Chance constraints provide a principled framework to mitigate the risk of high-impact extreme events by modifying the controllable properties of a system. The low probability and r…
A Machine-Learning-Based Importance Sampling Method to Compute Rare Event Probabilities
Vishwas Rao, Romit Maulik, Emil Constantinescu +1
We develop a novel computational method for evaluating the extreme excursion probabilities arising from random initialization of nonlinear dynamical systems. The method uses excurs…
Sequential Bayesian Parameter Estimation of Stochastic Dynamic Load Models
Daniel Adrian Maldonado, Vishwas Rao, Mihai Anitescu +1
In this paper we focus on the parameter estimation of dynamic load models with stochastic terms, in particular, load models where protection settings are uncertain, such as in aggr…
Efficient computation of extreme excursion probabilities for dynamical systems
Vishwas Rao, Mihai Anitescu
We develop a novel computational method for evaluating the extreme excursion probabilities arising for random initialization of nonlinear dynamical systems. The method uses a Marko…
Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models
Romit Maulik, Vishwas Rao, Sandeep Madireddy +2
Rapid simulations of advection-dominated problems are vital for multiple engineering and geophysical applications. In this paper, we present a long short-term memory neural network…
Scalable matrix-free adaptive product-convolution approximation for locally translation-invariant operators
Nick Alger, Vishwas Rao, Aaron Myers +2
We present an adaptive grid matrix-free operator approximation scheme based on a "product-convolution" interpolation of convolution operators. This scheme is appropriate for operat…
A Learning Based Approach for Uncertainty Analysis in Numerical Weather Prediction Models
Azam Moosavi, Vishwas Rao, Adrian Sandu
Complex numerical weather prediction models incorporate a variety of physical processes, each described by multiple alternative physical schemes with specific parameters. The selec…
A Hybrid Monte-Carlo Sampling Smoother for Four Dimensional Data Assimilation
Ahmed Attia, Vishwas Rao, Adrian Sandu
This paper constructs an ensemble-based sampling smoother for four-dimensional data assimilation using a Hybrid/Hamiltonian Monte-Carlo approach. The smoother samples efficiently f…
A Time-parallel Approach to Strong-constraint Four-dimensional Variational Data Assimilation
Vishwas Rao, Adrian Sandu
A parallel-in-time algorithm based on an augmented Lagrangian approach is proposed to solve four-dimensional variational (4D-Var) data assimilation problems. The assimilation windo…
A-posteriori error estimates for inverse problems
Vishwas Rao, Adrian Sandu
Inverse problems use physical measurements along with a computational model to estimate the parameters or state of a system of interest. Errors in measurements and uncertainties in…