Ramakrishna Tipireddy

According to our database, Ramakrishna Tipireddy authored at least 17 papers between 2014 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

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, Alexandre M. Tartakovsky

CoRR, 2023

Accelerating Scientific Simulations with Bi-Fidelity Weighted Transfer Learning.

Katarzyna Borowiec, Dan Lu, Vikas Chandan, Samrat Chatterjee, Pradeep Ramuhalli, Ramakrishna Tipireddy, Mahantesh Halappanavar, Frank Liu

Proceedings of the International Conference on Machine Learning and Applications, 2023

Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti's Theorem for Markov Chains.

Buddhika Nettasinghe, Samrat Chatterjee, Ramakrishna Tipireddy, Mahantesh M. Halappanavar

Proceedings of the International Conference on Machine Learning, 2023

2021

Lorenz System State Stability Identification using Neural Networks.

Megha Subramanian, Ramakrishna Tipireddy, Samrat Chatterjee

CoRR, 2021

Time-dependent stochastic basis adaptation for uncertainty quantification.

Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky

CoRR, 2021

2020

FPDetect: Efficient Reasoning About Stencil Programs Using Selective Direct Evaluation.

Arnab Das, Sriram Krishnamoorthy, Ian Briggs, Ganesh Gopalakrishnan, Ramakrishna Tipireddy

ACM Trans. Archit. Code Optim., 2020

Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models.

Ramakrishna Tipireddy, David A. Barajas-Solano, Alexandre M. Tartakovsky

J. Comput. Phys., 2020

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 M. Tartakovsky, Ramakrishna Tipireddy

CoRR, 2020

Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids.

Tong Ma, David Alonso Barajas-Solano, Ramakrishna Tipireddy, Alexandre M. Tartakovsky

CoRR, 2020

Bayesian phase estimation with adaptive grid refinement.

Ramakrishna Tipireddy, Nathan Wiebe

CoRR, 2020

2019

Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression.

Tong Ma, Renke Huang, David A. Barajas-Solano, Ramakrishna Tipireddy, Alexandre M. Tartakovsky

CoRR, 2019

A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations.

Ramakrishna Tipireddy, Paris Perdikaris, Panos Stinis, Alexandre M. Tartakovsky

CoRR, 2019

Physics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids.

Alexandre M. Tartakovsky, Ramakrishna Tipireddy

Proceedings of the 52nd Hawaii International Conference on System Sciences, 2019

2018

Stochastic Basis Adaptation and Spatial Domain Decomposition for Partial Differential Equations with Random Coefficients.

Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky

SIAM/ASA J. Uncertain. Quantification, 2018

Quantification, Trade-off Analysis, and Optimal Checkpoint Placement for Reliability and Availability.

Omer Subasi, Ramakrishna Tipireddy, Sriram Krishnamoorthy

Proceedings of the 25th IEEE International Conference on High Performance Computing, 2018

2017

Basis adaptation and domain decomposition for steady-state partial differential equations with random coefficients.

Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky

J. Comput. Phys., 2017

2014

Basis adaptation in homogeneous chaos spaces.

Ramakrishna Tipireddy, Roger G. Ghanem

J. Comput. Phys., 2014