In the News
Extraction of mechanical properties of materials through deep learning from instrumented indentation
George Karniadakis with his PhD senior student Lu Lu and collaborators from MIT and NTU (Singapore) have implemented a deep learning method named instrumented indentation as a means of extracting material properties from 3D printed materials. The properties of such materials (titanium and aluminum alloys) are very different than traditionally made materials, and it is difficult to infer them using existing methods. (Read more.)
Professor George Em Karnadakis from Brown University, in collaboration with Caltech, Stanford University, and the University of Utah, have been awarded an AFOSR MURI grant for their work in, “Learning and Meta-Learning of Partial Differential Equations via Physics-Informed Neural Networks: Theory, Algorithms, and Applications. This work seeks to overcome mathematical obstacles by introducing physics-informed learning, integrating seamlessly data and mathematical models, and implementing them using physics-informed neural networks (PINNS) and other new physics-informed networks (PINs).
Sickle Cell Disease affects approximately 100,000 people in the United States and millions worldwide. African American experience this disease most prevalently, afflicting one out of every 365 babies. New drug therapies are urgently needed, however the development of a novel drug requires sometimes four to six years of experimentation. (Read full story.)
Professor George Karniadakis in collaboration with Maziar Raissi and Alireza Yazdani have developed an algorithm which could potentially be used to analyze magnetic resonance imagery of blood flow through a brain aneurysm and compute the stress being placed on an arterial wall. Read full story and Science article.