Paris Perdikaris, assistant professor in the Department of Mechanical Engineering and Applied Mechanics, has been honored with an Early Career Prize from the Society for Industrial and Applied Mathematics (SIAM).
Perdikaris’s prize comes from the SIAM Activity Group on Computational Science and Engineering (SIAG/CSE), and is in recognition of “his work on machine learning using Gaussian processes and neural networks, which has set the foundation for a new paradigm in data-driven and physics-informed scientific computing.” He will present a virtual lecture on the topic at the SIAM Conference on Computational Science and Engineering next month.
SIAG/CSE awards a single Early Career Prize every two years, recognizing researchers in the field of computational science and engineering who have made influential contributions to their field within seven years of receiving their Ph.D. or equivalent degree.
In an interview with SIAM News, Perdikaris described the potential impact of the research that earned this honor.
Thanks to the rapid development of sensor networks we are now able to exploit wealth of variable fidelity observations using data-driven methods. However, our growing ability to collect and create observational data far outpaces our ability to sensibly assimilate it, let alone understand it. To this end, our work aims to leverage fundamental physical laws and domain knowledge to “teach” machine learning models about governing physical rules, with the goal of enhancing our ability to interpret and expediently predict the behavior of complex systems across diverse disciplines and applications, from climate modeling and geophysics, to materials characterization, fluid dynamics and biophysics.
Continue reading Perdikaris’s interview at SIAM News.