Karniadakis Elected to the American Academy of Arts and Sciences

George Karniadakis, who is a professor of applied mathematics at Brown University and holds a joint appointment with Pacific Northwest National Laboratory (PNNL), has been elected to the American Academy of Arts and Sciences. This prestigious society has been composed of leaders and high-achieving individuals in various fields, including science and technology, since its inception in 1780.

Karniadakis was honored for his work in applied mathematics. In 2023, he was named one of the “Best Mathematics Scientists” by Research.com. His work, among many other topics, includes research on physics-informed neural networks and deep operator networks.

“For over a decade, George Karniadakis has been pushing the research envelope at PNNL, from modeling complex systems to scientific machine learning, while educating a new generation of applied mathematicians,” said Panos Stinis, leader of the Computational Mathematics group.

Since 2013, Karniadakis has been a joint appointee in the Computational Mathematics group in the Advanced Computing, Mathematics, and Data Division at PNNL. Since then, he has led the establishment of three Mathematical Multifaceted Integrated Capability Centers (MMICCs), allowing PNNL to become the only Department of Energy laboratory with three successive centers.

First, the Collaboratory on Mathematics for Mesoscopic Modeling of Materials (CM4) aimed to develop modeling methods and scalable algorithms inspired by problems in fluid dynamics and soft matter physics.

Next, the Physics-Informed Learning Machines for Multiscale and Multiphysics Problems (PhILMs) Center advanced the field of physics with new developments in deep learning, a computing technique that harnesses the power of machine learning and big data. This MMICC was a collaboration among PNNL, Sandia National Laboratories, and academic partners at Brown University, the Massachusetts Institute of Technology, Stanford University, and the University of California, Santa Barbara.

Today, Karniadakis leads the Scalable, Efficient and Accelerated Causal Reasoning Operators, Graphs and Spikes for Earth and Embedded Systems (SEA-CROGS) Center, which accelerates modeling, inference, causal reasoning, etiology, and pathway discovery for Earth systems, embedded systems, mobile platforms, and others using physics-informed machine learning.

In 1984 and 1987, respectively, Karniadakis received his master’s degree and PhD in mechanical engineering from the Massachusetts Institute of Technology. Prior to this, he earned his bachelor’s degree in mechanical engineering and naval architecture from the National Technical University of Athens in 1982.