Publications and Notes

Publications

• John Darges, Alen Alexanderian, Pierre Gremaud. Variance-based sensitivity of Bayesian inverse problems to the prior distribution. International Journal for Uncertainty Quantification. 2024.
• John Darges, Alen Alexanderian, Pierre Gremaud. Extreme learning machines for variance-based global sensitivity analysis. International Journal for Uncertainty Quantification. 2024. [Link]
• Jun Hu, Zhenkun Guo, Peter E Mcwilliams, John E Darges, Daniel L Druffel, Andrew M Moran, Scott C Warren. Band gap engineering in a 2D material for solar-to-chemical energy conversion. Nano Letters. 2016. [Link]

Presentations

• Randomized function approximation. North Carolina State University. Raleigh, NC, USA. Applied Mathematics Graduate Student Seminar. November 2023. [Link]
• Variance-based sensitivity of Bayesian inverse problems to the prior distribution. North Carolina State University. Raleigh, NC, USA. Research Training Group. October 2023. [Link]
• Identifying important prior hyperparameters in Bayesian inverse problems with efficient variance-based global sensitivity analysis. North Carolina State University, Raleigh, NC, USA. Applied Mathematics Graduate Student Seminar. April 2023. [Link]
• Extreme learning machines for variance-based global sensitivity analysis. RAI Amsterdam Convention Center, Amsterdam, Netherlands. SIAM Conference on Computational Science and Engineering. March 2023. [Link]
• Extreme learning machines for variance-based global sensitivity analysis. Walter E. Washington Convention Center, Washington, D.C., USA. Joint Statistical Meetings. August 2022. [Link]
• Extreme learning machines for variance-based global sensitivity analysis. Florida State University, Tallahassee, FL, USA. Conference on Sensitivity Analysis of Model Output (SAMO). March 2022. [Link]

Research Notes

• Measuring the additivity of functions with variance-based global sensitivity analysis. 2022. [Link]
• On the approximation of higher order Sobol’ indices with ELM surrogates. 2021. [Link]
• Global sensitivity analysis for optimization under uncertainty. 2020. [Link]

Course Notes and Projects

• Uncertainty quantification for heat transfer in turbulent flow. 2021. [Link]
• Classifying the winning player in Connect Four with machine learning. 2020. [Link]
• Introduction to sub-Riemannian geometry. 2020. [Link]
• Riemannian structure on Lie groups. 2020. [Link]