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: Introduction to the SimCenter and the quoFEM application: basic capabilities including Latin Hypercube Sampling, Monte Carlo Sampling, and Sensitivity Analysis; an introduction to the Custom UQ Engine - a new feature that invites advanced users to integrate and use their tools within the SimCenter's Application Framework, and the benefits of Gaussian process-based surrogate modeling


quoFEM Day 2: Introduction to the custom FEM engine with demonstrations of two structural engineering examples. The first will drive ETABS to calibrate a moment frame structure using modal characteristics. The second example calibrates an MDOF model run through Matlab.

Tool Training Workshop: May 24-25, 2021

quoFEM Day 1: New features in quoFEM: Global Sensitivity Analysis and Bayesian Calibration


quoFEM Day 2: A presentation of quoFEM's advanced features and backend workflow

Tool Training Workshop: June 15-16, 2020

quoFEM Day 1: An Introduction to the the SimCenter, examples of research applications the SimCenter has developed, educational software and an overview of quoFEM


quoFEM Day 2: A demonstration of how quoFEM works with Python scripts, OpenSeesPy or other custom scripts. Calibration examples: conventional and Bayesian

Join the User Forum Conversation:

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

Recent Updates:

  • Support for a gradient-free optimization method
  • Support for stochastic Kriging
  • Fast global sensitivity analysis for very high dimensional output (tested on 2 million QoIs)
  • New option to discard working directories after each model simulation
  • Significantly enhanced speed of surrogate validation and prediction
  • "None" option for FEM
  • Major renaming: (1) OpenseesPy->python (FEM), (2) Parameters estimation -> deterministic calibration (UQ/Dakota), (3) Inverse problem -> Bayesian calibration (UQ/Dakota)


How to cite:

Frank McKenna, Sang-ri Yi, Aakash Bangalore Satish, Adam Zsarnoczay, Michael Gardner, & Wael Elhaddad. (2022). NHERI-SimCenter/quoFEM: Version 3.2.0 (v3.2.0). Zenodo. https://doi.org/10.5281/zenodo.7131444

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.