The Quantified Uncertainty with Optimization for the Finite Element Method (quoFEM) application (formerly uqFEM) is intended to advance the use of uncertainty quantification and optimization within the field natural hazards engineering. The application achieves this by combining existing finite element applications, e.g. FEAPpv, with uncertainty quantification (UQ) applications, e.g. Dakota, behind a simple user interface (UI). The combined application will enhance the finite element applications with uncertainty quantification and optimization capabilities. To overcome the issue of computational overhead, which typically precludes these types of probabilistic analysis from being performed, the user has the option of specifying that the simulations take place on HPC resources, e.g. the TACC Stampede supercomputer made available through DesignSafe-ci.

The specific types of analyses that the quoFEM application provides are:

  • Uncertainty Quantification
  • Calibration and Bayesian Calibration
  • Optimization
  • Sensitivity

Replacing a computationally expensive simulation with a Gaussian process surrogate model
in quoFEM, Sang-ri Yi, Postdoctoral Researcher, UC Berkeley, April 1, 2022

Global Sensitivity Analysis on a Structural System
Sang-ri Yi, Postdoctoral Researcher, UC Berkeley
November 12, 2021

Calibrating an OpenSees Material
Model using Experimental Data
Aakash Bangalore Satish PhD, UC Berkeley
December 10, 2021

Tool Training Workshop: February 22-23, 2022

quoFEM Day 1


quoFEM Day 2

Tool Training Workshop: May 24-25, 2021

quoFEM Day 1


quoFEM Day 2

Tool Training Workshop: June 15-16, 2020

quoFEM Day 1


quoFEM Day 2

Run at DesignSafe
 • must be logged in before clicking
 • re-enter DesignSafe login on DCV page

Join the User Forum Conversation:

  • submit questions and get answers
  • provide user feedback
  • post feature requests
  • submit bug reports

Recent Updates:

  • Cloud-enabled jobs now run on the Frontera supercomputer at DesignSafe
  • New option for surrogate modeling using Probabilistic Learning on Manifolds (PLoM)
  • Restructured surrogate model scripts
  • Improvements to the message area
  • Major restructuring of the backend
  • Minor bug fixes in the user interface, surrogate modeling and sensitivity analysis scripts
  • Updated example files


How to cite:

Frank McKenna, Sang-ri Yi, Aakash Bangalore Satish, Adam Zsarnoczay, Michael Gardner, Kuanshi Zhong, & Wael Elhaddad. (2022). NHERI-SimCenter/quoFEM: Version 3.0.0 (v3.0.0). Zenodo. https://doi.org/10.5281/zenodo.6404498

Deierlein, G.G., McKenna, F., et al. (2020). A Cloud-Enabled Application Framework for Simulating Regional-Scale Impacts of Natural Hazards on the Built Environment. Frontiers in Built Environment. 6, 196. doi: 10.3389/fbuil.2020.558706.