34 citations · 82 across the 15 of their papers we have counts for
15 papers
Conditional Korhunen-Loéve regression model with Basis Adaptation for high-dimensional problems: uncertainty quantification and inverse modeling
Yu-Hong Yeung, Ramakrishna Tipireddy, David A. Barajas-Solano +1
We propose a methodology for improving the accuracy of surrogate models of the observable response of physical systems as a function of the systems' spatially heterogeneous paramet…
Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti's Theorem for Markov Chains
Buddhika Nettasinghe, Samrat Chatterjee, Ramakrishna Tipireddy +1
Conformal prediction is a widely used method to quantify the uncertainty of a classifier under the assumption of exchangeability (e.g., IID data). We generalize conformal predictio…
Lorenz System State Stability Identification using Neural Networks
Megha Subramanian, Ramakrishna Tipireddy, Samrat Chatterjee
Nonlinear dynamical systems such as Lorenz63 equations are known to be chaotic in nature and sensitive to initial conditions. As a result, a small perturbation in the initial condi…
Time-dependent stochastic basis adaptation for uncertainty quantification
Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky
We extend stochastic basis adaptation and spatial domain decomposition methods to solve time varying stochastic partial differential equations (SPDEs) with a large number of input…
An efficient epistemic uncertainty quantification algorithm for a class of stochastic models: A post-processing and domain decomposition framework
Mahadevan Ganesh, Stuart C Hawkins, Alexandre Tartakovsky +1
Partial differential equations (PDEs) are fundamental for theoretically describing numerous physical processes that are based on some input fields in spatial configurations. Unders…
Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids
Tong Ma, David Alonso Barajas-Solano, Ramakrishna Tipireddy +1
Real-time state estimation and forecasting is critical for efficient operation of power grids. In this paper, a physics-informed Gaussian process regression (PhI-GPR) method is pre…
Bayesian phase estimation with adaptive grid refinement
Ramakrishna Tipireddy, Nathan Wiebe
We introduce a novel Bayesian phase estimation technique based on adaptive grid refinement method. This method automatically chooses the number particles needed for accurate phase…
FPDetect: Efficient Reasoning About Stencil Programs Using Selective Direct Evaluation
Arnab Das, Sriram Krishnamoorthy, Ian Briggs +2
We present FPDetect, a low overhead approach for detecting logical errors and soft errors affecting stencil computations without generating false positives. We develop an offline a…
Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression
Tong Ma, Renke Huang, David Barajas-Solano +2
We propose a new forecasting method for predicting load demand and generation scheduling. Accurate week-long forecasting of load demand and optimal power generation is critical for…
Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models
Ramakrishna Tipireddy, David A Barajas-Solano, Alexandre M. Tartakovsky
We use a conditional Karhunen-Loève (KL) model to quantify and reduce uncertainty in a stochastic partial differential equation (SPDE) problem with partially-known space-dependent…
Solvers and precondtioners based on Gauss-Seidel and Jacobi algorithms for non-symmetric stochastic Galerkin system of equations
Ramakrishna Tipireddy, Eric T. Phipps, Roger G. Ghanem
In this work, solvers and preconditioners based on Gauss-Seidel and Jacobi algorithms are explored for stochastic Galerkin discretization of partial differential equations (PDEs) w…
A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations
Ramakrishna Tipireddy, Paris Perdikaris, Panos Stinis +1
We investigate the use of discrete and continuous versions of physics-informed neural network methods for learning unknown dynamics or constitutive relations of a dynamical system.…
Physics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids
Ramakrishna Tipireddy, Alexandre Tartakovsky
We present a physics-informed Gaussian Process Regression (GPR) model to predict the phase angle, angular speed, and wind mechanical power from a limited number of measurements. In…
Stochastic basis adaptation and spatial domain decomposition for PDEs with random coefficients
Ramakrishna Tipireddy, Panos Stinis, Alexandre Tartakovsky
We present a novel uncertainty quantification approach for high-dimensional stochastic partial differential equations that reduces the computational cost of polynomial chaos method…
Basis adaptation and domain decomposition for steady partial differential equations with random coefficients
Ramkrishna Tipireddy, Panos Stinis, Alexandre Tartakovsky
We present a novel approach for solving steady-state stochastic partial differential equations (PDEs) with high-dimensional random parameter space. The proposed approach combines s…