FOR IMMEDIATE RELEASE – PRESS RELEASE
Prof. George Karniadakis is one of the most well-known scholars working at interface of Computational Mathematics + Machine Learning + X, where X may be problems in biology, geophysics, soft matter, functional materials, physical chemistry, or fluid and solid mechanics.
Boston, MA – August 23, 2023 – PredictiveIQ, a leading startup pioneering Small Data AI powered Digital Twins announces that world-renowned Prof. George Karniadakis has joined its Board of Advisors. Prof. Karniadakis is a Charles Pitt Robinson & John Palmer Barstow Professor of Applied Mathematics & Engineering at Brown University, a Research Scientist of Mechanical Engineering at MIT, and Center Director for a Mathematics Consortium on physics-informed machine intelligence at Pacific Northwest National Laboratory (PNNL). Dr. Karniadakis is a member of the National Academy of Engineering and a Vannevar Bush Faculty Fellow.
Dr. Juan F. Betts, CEO at PredictiveIQ, said, “The addition of Prof. Karniadakis to our Board of Advisors will add to our technical leadership in advanced data-driven and physics informed machine learning and AI methods that power our Digital Twins. Prof. Karniadakis’ involvement will strengthen our commitment to excellence and innovation in delivering advanced predictive AI models.”
Professor Karniadakis said, “I look forward to working with the PredictiveIQ team to further advance the state of the art in this emerging field of Physics Informed Learning Machines. PredictiveIQ has some interesting projects that are at the bleeding edge of this technology that are ripe for wider commercial adoption.”
Dr. Karniadakis has pioneered the field of Physics Informed Machine Learning (PIML). His h-index is 134 and he has been cited over 87,000 times. Dr. Karniadakis received his S.M. and Ph.D. from Massachusetts Institute of Technology (1984/87). He was appointed Lecturer in the Department of Mechanical Engineering at MIT and subsequently he joined the Center for Turbulence Research at Stanford / Nasa Ames. Dr. Karniadakis joined Princeton University as Assistant Professor in the Department of Mechanical and Aerospace Engineering and as Associate Faculty in the Program of Applied and Computational Mathematics. He was a Visiting Professor at Caltech in 1993 in the Aeronautics Department and joined Brown University as Associate Professor of Applied Mathematics in the Center for Fluid Mechanics in 1994. After becoming a full professor in 1996, he continued to be a Visiting Professor and Senior Lecturer of Ocean/Mechanical Engineering at MIT. He is an AAAS Fellow (2018-), Fellow of the Society for Industrial and Applied Mathematics (SIAM, 2010-), Fellow of the American Physical Society (APS, 2004-), Fellow of the American Society of Mechanical Engineers (ASME, 2003-) and Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA, 2006-). Dr. Karniadakis received the SIAM/ACM Prize on Computational Science & Engineering (2021), the Alexander von Humboldt award in 2017, the SIAM Ralf E Kleinman award (2015), the J. Tinsley Oden Medal (2013), and the CFD award (2007) by the US Association in Computational Mechanics.
To learn more about Prof. Karniadakis visit:
https://sites.brown.edu/crunch-group/
About PredictiveIQ.
PredictiveIQ develops Small Data AI powered Digital Twins. Small Data AI utilizes advanced data-driven neural concepts and physics informed machine learning (PIML) approaches that reduce by orders of magnitude (1,000X) the amount of ML training data, increases predictive accuracy, improves generalization, and enables modular updating. This capability can be deployed, embedded, on-edge or on-cloud, providing our customers with real-time actionable decision making. A leading application for this capability is Predictive Maintenance Digital Twins, which can be applied in industries such as: Defense, Mining, Transportation, Oil & Gas, Agriculture, Marine, Automotive, and Aerospace.
For more information visit our website https://www.predictiveiq.com/