The Division of Applied Mathematics


The mission of the Division of Applied Mathematics rests on research, education, and scholarship. We focus our research and teaching on a wide range of areas from applied and algorithmic problems to the study of fundamental mathematical questions. In particular, we explore the connections between mathematics and its applications in biology, chemistry, engineering, geosciences, neuroscience, physics and other disciplines at the research and educational levels. Our educational efforts are realized primarily through our graduate PhD program and our four undergraduate concentrations.  

Important note for sophomores: 

If you have any questions about your declaration, please email [email protected].

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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.)

Kavita Ramanan has been selected as a Fellow of the Society for Industrial and Applied Mathematics

Kavita Ramanan has been selected as a Fellow of the Society for Industrial and Applied Mathematics.  Professor Ramanan is being recognized for her contributions to constrained and reflected processes and stochastic networks.  (Read more.)

Learning and Meta-Learning of Partial Differential Equations via Physics-Informed Neural Networks

Professor George Em Karnadakis from Brown University, in collaboration with Caltech, Stanford University, and the University of Utah, has 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 seamless data and mathematical models, and implementing them using physics-informed neural networks (PINNS) and other new physics-informed networks (PINs).

In Silico Medicine Advances the Development of Sickle Cell Disease Therapies

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.)

Researchers are unlocking the genetic rules that direct the stripes of zebrafish

Researchers at Brown University in the Division of Applied Mathematics have been working on an algorithm that can help them describe the various attributes of shapes and patterns, enabling the researchers to test ideas about how zebrafish stripes are formed.  Professor Bjorn Sandstede, Alexandra Volkening and Melissa McGuirl are collaborating on this endeavor, and have published their findings in the Proceedings of the National Academy of Sciences.