Ramakrishna Tipireddy

Publication Activity (10 Years)

Years Active: 2014-2023
Publications (10 Years): 18

Top Topics

Stochastic Models

Gaussian Process

Top Venues

J. Comput. Phys.

SIAM/ASA J. Uncertain. Quantification

Publications

Buddhika Nettasinghe, Samrat Chatterjee, Ramakrishna Tipireddy, Mahantesh M. Halappanavar
Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti's Theorem for Markov Chains. ICML (2023)
Yu-Hong Yeung, Ramakrishna Tipireddy, David A. Barajas-Solano, Alexandre M. Tartakovsky
Conditional Korhunen-Loéve regression model with Basis Adaptation for high-dimensional problems: uncertainty quantification and inverse modeling. CoRR (2023)
Katarzyna Borowiec, Dan Lu, Vikas Chandan, Samrat Chatterjee, Pradeep Ramuhalli, Ramakrishna Tipireddy, Mahantesh Halappanavar, Frank Liu
Accelerating Scientific Simulations with Bi-Fidelity Weighted Transfer Learning. ICMLA (2023)
Buddhika Nettasinghe, Samrat Chatterjee, Ramakrishna Tipireddy, Mahantesh Halappanavar
Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti's Theorem for Markov Chains. CoRR (2022)
Megha Subramanian, Ramakrishna Tipireddy, Samrat Chatterjee
Lorenz System State Stability Identification using Neural Networks. CoRR (2021)
Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky
Time-dependent stochastic basis adaptation for uncertainty quantification. CoRR (2021)
Tong Ma, David Alonso Barajas-Solano, Ramakrishna Tipireddy, Alexandre M. Tartakovsky
Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids. CoRR (2020)
Arnab Das, Sriram Krishnamoorthy, Ian Briggs, Ganesh Gopalakrishnan, Ramakrishna Tipireddy
FPDetect: Efficient Reasoning About Stencil Programs Using Selective Direct Evaluation. CoRR (2020)
Ramakrishna Tipireddy, Nathan Wiebe
Bayesian phase estimation with adaptive grid refinement. CoRR (2020)
Ramakrishna Tipireddy, David A. Barajas-Solano, Alexandre M. Tartakovsky
Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models. J. Comput. Phys. 418 (2020)
Mahadevan Ganesh, Stuart C. Hawkins, Alexandre M. Tartakovsky, Ramakrishna Tipireddy
An efficient epistemic uncertainty quantification algorithm for a class of stochastic models: A post-processing and domain decomposition framework. CoRR (2020)
Arnab Das, Sriram Krishnamoorthy, Ian Briggs, Ganesh Gopalakrishnan, Ramakrishna Tipireddy
FPDetect: Efficient Reasoning About Stencil Programs Using Selective Direct Evaluation. ACM Trans. Archit. Code Optim. 17 (3) (2020)
Alexandre M. Tartakovsky, Ramakrishna Tipireddy
Physics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids. HICSS (2019)
Ramakrishna Tipireddy, Paris Perdikaris, Panos Stinis, Alexandre M. Tartakovsky
A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations. CoRR (2019)
Tong Ma, Renke Huang, David A. Barajas-Solano, Ramakrishna Tipireddy, Alexandre M. Tartakovsky
Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression. CoRR (2019)
Omer Subasi, Ramakrishna Tipireddy, Sriram Krishnamoorthy
Quantification, Trade-off Analysis, and Optimal Checkpoint Placement for Reliability and Availability. HiPC (2018)
Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky
Stochastic Basis Adaptation and Spatial Domain Decomposition for Partial Differential Equations with Random Coefficients. SIAM/ASA J. Uncertain. Quantification 6 (1) (2018)
Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky
Basis adaptation and domain decomposition for steady-state partial differential equations with random coefficients. J. Comput. Phys. 351 (2017)
Ramakrishna Tipireddy, Roger G. Ghanem
Basis adaptation in homogeneous chaos spaces. J. Comput. Phys. 259 (2014)