News

Dr. Ying Zhao successfully defended her Ph.D. dissertation entitled “Machine Learning-Based Calibration of Streamflow Prediction Model“. Congratulations!!

Dr. Zhen Hu has been honored with the 2023 CECS Faculty Excellence in Research Award by the College of Engineering and Computer Science at the UM-Dearborn. Excellence in Research Award is granted to faculty who demonstrate excellence in research, scholarly Read more…

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability improvement of ML models empowered by UQ Read more…

The use of unmanned aerial vehicles (UAVs) for structural health inspection has become a promising technique to perform labor-intensive, accessibility-challenged, and sometimes dangerous inspection tasks. This paper presents a novel physics-informed UAV inspection planning framework for infrastructure structural health assessment based on Read more…

Path planning plays a vital role in ensuring the efficient and safe operation of off-road autonomous ground vehicles (AGVs). Current methods mostly focus on minimizing the travel time or path length of AGVs and often overlook the fact that the Read more…

Likelihood-free inference methods have been widely adopted, but they face significant challenges in updating multi-level computational models that have hierarchically embedded sub-models. This difficulty arises from the lack of direct observations of the quantities of interest of the sub-models. In Read more…

Dr. Zhen Hu has been awarded the ASME Design Automation Young Investigator Award. The award was presented during ASME’s 2023 International Design Engineering Technical Conference (IDETC). The Design Automation Young Investigator Award is given from time to time, but never more than once each year, Read more…

Reliability-based global path planning incorporates reliability constraints into path planning to ensure that off-road autonomous ground vehicles can operate reliably in uncertain off-road environments. Current two-stage reliability-based path planning methods involve separate stages for surrogate modeling of mobility prediction and Read more…

Corrosion morphology prediction for civil infrastructure using physics-based simulation is computationally challenging due to the coupled multi-physics simulations involved in long-term corrosion prognostics. Machine learning (ML)-based surrogate modeling provides a promising way of overcoming this challenge. This paper presents a Read more…