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Ramakrishna Tipireddy
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2020 – today
- 2023
- [c4]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: 25890-25903 - [c3]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: 994-999 - [i10]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 abs/2307.02572 (2023) - 2022
- [i9]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 abs/2210.02271 (2022) - 2021
- [i8]Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky:
Time-dependent stochastic basis adaptation for uncertainty quantification. CoRR abs/2103.03316 (2021) - [i7]Megha Subramanian, Ramakrishna Tipireddy, Samrat Chatterjee:
Lorenz System State Stability Identification using Neural Networks. CoRR abs/2106.08489 (2021) - 2020
- [j5]Ramakrishna Tipireddy
, David A. Barajas-Solano
Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models. J. Comput. Phys. 418: 109604 (2020) - [j4]Arnab Das
FPDetect: Efficient Reasoning About Stencil Programs Using Selective Direct Evaluation. ACM Trans. Archit. Code Optim. 17(3): 19:1-19:27 (2020) - [i6]Arnab Das, Sriram Krishnamoorthy, Ian Briggs, Ganesh Gopalakrishnan, Ramakrishna Tipireddy:
FPDetect: Efficient Reasoning About Stencil Programs Using Selective Direct Evaluation. CoRR abs/2004.04359 (2020) - [i5]Ramakrishna Tipireddy, Nathan Wiebe:
Bayesian phase estimation with adaptive grid refinement. CoRR abs/2009.07898 (2020) - [i4]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 abs/2010.04591 (2020) - [i3]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 abs/2010.07863 (2020)
2010 – 2019
- 2019
- [c2]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: 1-7 - [i2]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 abs/1904.04058 (2019) - [i1]Tong Ma, Renke Huang, David A. Barajas-Solano, Ramakrishna Tipireddy, Alexandre M. Tartakovsky:
Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression. CoRR abs/1910.03783 (2019) - 2018
- [j3]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): 273-301 (2018) - [c1]Omer Subasi, Ramakrishna Tipireddy, Sriram Krishnamoorthy:
Quantification, Trade-off Analysis, and Optimal Checkpoint Placement for Reliability and Availability. HiPC 2018: 183-192 - 2017
- [j2]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: 203-215 (2017) - 2014
- [j1]Ramakrishna Tipireddy, Roger G. Ghanem
Basis adaptation in homogeneous chaos spaces. J. Comput. Phys. 259: 304-317 (2014)
Coauthor Index
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