New prediction model could improve response to wildfires and other environmental events

The behavior of massive wildland fires is incredibly complex, but as the western U.S. experiences these events with increasing frequency, the need to better understand those systems has become crucial.

Civil & Environmental Engineering Assistant Professor Hamed Ebrahimian is pioneering new scientific computation techniques to enhance prediction, decision-making and safety in critical areas such as earthquake engineering and wildfire prediction.

With support from the National Science Foundation (NSF) Faculty Early Career Development Program (CAREER), Ebrahimian is working to transform how machine-learning and physics-based computational models can be integrated to create “smart digital twins”: advanced virtual replicas of dynamic systems that can learn and adapt using measurement data.

The NSF CAREER program supports early-career faculty who have the potential to serve as academic role models in research and education.

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