NSF PAR Search | NSF Public Access Repository

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

Simulation model calibration with dynamic stratification and adaptive sampling

https://doi.org/10.1080/17477778.2024.2420807

Jain, Pranav; Shashaani, Sara; Byon, Eunshin (November 2024, Journal of Simulation)

Full Text Available
Dynamic Stratification and Post-Stratified Adaptive Sampling for Simulation Optimization

https://doi.org/10.1109/WSC60868.2023.10408173

Jain, Pranav; Shashaani, Sara (December 2023, IEEE)

Full Text Available
Wake effect parameter calibration with large-scale field operational data using stochastic optimization

https://doi.org/10.1016/j.apenergy.2023.121426

Jain, Pranav; Shashaani, Sara; Byon, Eunshin (October 2023, Applied Energy)

Full Text Available
A systematic comparison between FEBio and PolyFEM for biomechanical systems

https://doi.org/10.1016/j.cmpb.2023.107938

Martin, Liam; Jain, Pranav; Ferguson, Zachary; Gholamalizadeh, Torkan; Moshfeghifar, Faezeh; Erleben, Kenny; Panozzo, Daniele; Abramowitch, Steven; Schneider, Teseo (February 2024, Computer Methods and Programs in Biomedicine)

Full Text Available
High-Order Incremental Potential Contact for Elastodynamic Simulation on Curved Meshes

Ferguson, Zachary; Jain, Pranav; Zorin, Denis; Schneider, Teseo; Panozzo, Daniele (January 2023, SIGGRAPH Conference Track)

Full Text Available
Wake Eﬀect Calibration in Wind Power Systems with Adaptive Sampling based Optimization

Jain, Pranav; Shashaani, Sara; Byon, Eunshin. (January 2021, IIE Annual Conference. Proceedings)

The calibration of the wake effect in wind turbines is computationally expensive and with high risk due to noise in the data. Wake represents the energy loss in downstream turbines, and characterizing it is essential to design wind farm layout and control turbines for maximum power generation. With big data, calibrating the wake parameters is a derivative-free optimization that can be computationally expensive. But with stochastic optimization combined with variance reduction, we can reach robust solutions by harnessing the uncertainty through two sampling mechanisms: the sample size and the sample choices. We do the former by generating a varying number of samples and the latter using the variance-reduced sampling methods.
more » « less
Full Text Available

Search for: All records