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Renganathan, S. Ashwin
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1

CAMERA: A method for cost-aware, adaptive, multifidelity, e..:

Ashwin Renganathan, S. ; Rao, Vishwas ; Navon, Ionel M.
Journal of Computational Physics.  472 (2023)  - p. 111698 , 2023
Link: https://doi.org/10.1016/..
 
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2

Sensitivity Analysis of the Overwing Nacelle Design Space:

Ahuja, Jai ; Ashwin Renganathan, S. ; Mavris, Dimitri N.
Journal of Aircraft.  , 2022
Link: https://doi.org/10.2514/..
 
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3

Data-driven wind turbine wake modeling via probabilistic ma..:

Ashwin Renganathan, S. ; Maulik, Romit ; Letizia, Stefano.
Neural Computing and Applications.  34 (2022)  8 - p. 6171-6186 , 2022
Link: https://doi.org/10.1007/..
 
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4

Data-Driven Deep Learning Emulators for Geophysical Forecas..:

, In: Computational Science – ICCS 2021; Lecture Notes in Computer Science,
Sastry, Varuni Katti ; Maulik, Romit ; Rao, Vishwas... - p. 433-446 , 2021
Link: https://doi.org/10.1007/..
 
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5

Enhanced data efficiency using deep neural networks and Gau..:

Renganathan, S. Ashwin ; Maulik, Romit ; Ahuja, Jai
Aerospace Science and Technology.  111 (2021)  - p. 106522 , 2021
Link: https://doi.org/10.1016/..
 
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6

Empirical Assessment of Deep Gaussian Process Surrogate Mod..:

Rajaram, Dushhyanth ; Puranik, Tejas G. ; Ashwin Renganathan, S....
Journal of Aircraft.  58 (2021)  1 - p. 182-196 , 2021
Link: https://doi.org/10.2514/..
 
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7

qPOTS: Efficient batch multiobjective Bayesian optimization..:

Renganathan, S. Ashwin
http://arxiv.org/abs/2310.15788.  , 2023
Link: http://arxiv.org/abs/231..
 
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8

Contour Location for Reliability in Airfoil Simulation Expe..:

Booth, Annie S ; Renganathan, S. Ashwin ; Gramacy, Robert B
http://arxiv.org/abs/2308.04420.  , 2023
Link: http://arxiv.org/abs/230..
 
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9

CAMERA: A Method for Cost-aware, Adaptive, Multifidelity, E..:

Renganathan, S. Ashwin ; Rao, Vishwas ; Navon, Ionel M
http://arxiv.org/abs/2203.01436.  , 2022
Link: http://arxiv.org/abs/220..
 
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10

Data-Driven Wind Turbine Wake Modeling via Probabilistic Ma..:

Renganathan, S. Ashwin ; Maulik, Romit ; Letizia, Stefano.
http://arxiv.org/abs/2109.02411.  , 2021
Link: http://arxiv.org/abs/210..
 
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11

Machine-learning identification of the variability of mean ..:

Iungo, G. Valerio ; Maulik, Romit ; Renganathan, S. Ashwin.
http://arxiv.org/abs/2109.01646.  , 2021
Link: http://arxiv.org/abs/210..
 
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12

Lookahead Acquisition Functions for Finite-Horizon Time-Dep..:

Renganathan, S. Ashwin ; Larson, Jeffrey ; Wild, Stefan M
http://arxiv.org/abs/2105.09824.  , 2021
Link: http://arxiv.org/abs/210..
 
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13

Recursive Two-Step Lookahead Expected Payoff for Time-Depen..:

Renganathan, S. Ashwin ; Larson, Jeffrey ; Wild, Stefan
http://arxiv.org/abs/2006.08037.  , 2020
Link: http://arxiv.org/abs/200..
 
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14

Enhanced data efficiency using deep neural networks and Gau..:

Renganathan, S. Ashwin ; and, Romit Maulik ; Ahuja, Jai
http://arxiv.org/abs/2008.06731.  , 2020
Link: http://arxiv.org/abs/200..
 
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15

Aerodynamic Data Fusion Towards the Digital Twin Paradigm:

Renganathan, S. Ashwin ; Harada, Kohei ; Mavris, Dimitri N
http://arxiv.org/abs/1911.02924.  , 2019
Link: http://arxiv.org/abs/191..
 
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