activity
20172021
most citedLookahead Acquisition Functions for Finite-Horizon Time-Dependent Bayesian Optimization and Application to Quantum Optimal Control

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

stat.CO20211 cited

Lookahead Acquisition Functions for Finite-Horizon Time-Dependent Bayesian Optimization and Application to Quantum Optimal Control

S. Ashwin Renganathan, Jeffrey Larson, Stefan M. Wild

We propose a novel Bayesian method to solve the maximization of a time-dependent expensive-to-evaluate stochastic oracle. We are interested in the decision that maximizes the oracl…

physics.comp-ph2020

Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization

S. Ashwin Renganathan, Romit Maulik and, Jai Ahuja

Adjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space…

math.OC2020

Recursive Two-Step Lookahead Expected Payoff for Time-Dependent Bayesian Optimization

S. Ashwin Renganathan, Jeffrey Larson, Stefan Wild

We propose a novel Bayesian method to solve the maximization of a time-dependent expensive-to-evaluate oracle. We are interested in the decision that maximizes the oracle at a fini…

cs.CE2019

Aerodynamic Data Fusion Towards the Digital Twin Paradigm

S. Ashwin Renganathan, Kohei Harada, Dimitri N. Mavris

We consider the fusion of two aerodynamic data sets originating from differing fidelity physical or computer experiments. We specifically address the fusion of: 1) noisy and in-com…

math.OC2018

Koopman-Based Approach to Non-intrusive Projection-Based Reduced-Order Modeling with Black-Box High-Fidelity Models. Part II: Application

S. Ashwin Renganathan

A methodology for non-intrusive, projection-based non-linear model reduction originally presented by Renganathan et. al. (2018)~\cite{renganathan2018koopman} is further extended to…

math.AP2017

A Methodology for Projection-Based Model Reduction with Black-Box High-Fidelity Models

S. Ashwin Renganathan, Yingjie Liu, Dimitri N. Mavris

This paper presents a methodology that enables projection-based model reduction for black-box high-fidelity models such as commercial CFD codes. The methodology specifically addres…