Special Issue on Advanced Machine Learning for Uncertainty Quantification

Journal of Verification, Validation, and Uncertainty Quantification

Submit Paper

journal icon
With the increasing complexity of engineered systems, it becomes critically important to develop cost-effective system predictive models in reliability modeling, uncertainty quantification, and system design optimization for a multitude of reliability-critical applications. During the last decade, machine learning has shown potential in generating data-driven surrogate models that can be used to replace expensive physics-based models and support uncertainty quantification and continuous improvement of system design under uncertainty.

However, challenges persist in improving the fidelity of predictive models for the mimic of engineered systems due to the lack of expensive high-fidelity data. Thus, the main theme of this Special Issue is dedicated to the development of novel machine learning methods that shed new light on deeply integrating uncertainty quantification (UQ) methods with machine learning for solving engineering problems under uncertainties.

Topic Areas

  • High dimensional uncertainty quantification
  • Multi-fidelity data fusion for UQ using machine learning
  • Deep learning-based uncertainty quantification
  • Uncertainty quantification of machine learning models
  • Model verification and validation of machine learning
  • Model calibration and validation
  • Reliability modeling of engineered systems
  • Explainable machine learning-based methods
  • Applications of advanced machine learning methods for uncertainty quantification

Special Issue Publication Dates

Paper submission deadline: February 29, 2024
Publication date: June 2024

Submission Instructions

Papers should be submitted electronically to the journal through the ASME Journal Tool. If you already have an account, log in as an author and select Submit Paper. If you do not have an account, you can create one here

Once at the Paper Submittal page, select ASME Journal of Verification, Validation, and Uncertainty Quantification, and then select the Special Issue on Advanced Machine Learning for Uncertainty Quantification.

Papers received after the deadline or papers not selected for the Special Issue may be accepted for publication in a regular issue.

Guest Editors

Dr. Zequn Wang, Dept. of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, USA, (zequnw@mtu.edu)
Dr. Zhen Hu, Department of Industrial & Manufacturing Systems Engineering, University of Michigan-Dearborn, USA, (zhennhu@umich.edu)

You are now leaving ASME.org