most citedIntelligent sampling for surrogate modeling, hyperparameter optimization, and data analysis

32 citations · 36 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG2023

Spatio-Temporal Surrogates for Interaction of a Jet with High Explosives: Part II -- Clustering Extremely High-Dimensional Grid-Based Data

Chandrika Kamath, Juliette S. Franzman

Building an accurate surrogate model for the spatio-temporal outputs of a computer simulation is a challenging task. A simple approach to improve the accuracy of the surrogate is t…

cs.LG2023

Spatio-Temporal Surrogates for Interaction of a Jet with High Explosives: Part I -- Analysis with a Small Sample Size

Chandrika Kamath, Juliette S. Franzman, Brian H. Daub

Computer simulations, especially of complex phenomena, can be expensive, requiring high-performance computing resources. Often, to understand a phenomenon, multiple simulations are…

cs.LG20234 cited

Data Mining for Faster, Interpretable Solutions to Inverse Problems: A Case Study Using Additive Manufacturing

Chandrika Kamath, Juliette Franzman, Ravi Ponmalai

Solving inverse problems, where we find the input values that result in desired values of outputs, can be challenging. The solution process is often computationally expensive and i…

cs.LG202332 cited

Intelligent sampling for surrogate modeling, hyperparameter optimization, and data analysis

Chandrika Kamath

Sampling techniques are used in many fields, including design of experiments, image processing, and graphics. The techniques in each field are designed to meet the constraints spec…

physics.plasm-ph2023

Classification of Orbits in Poincaré Maps using Machine Learning

Chandrika Kamath

Poincaré plots, also called Poincaré maps, are used by plasma physicists to understand the behavior of magnetically confined plasma in numerical simulations of a tokamak. These plo…