I am currently a researcher in mathematics with focus on some theoretical and numerical investigations of deep learning in the context of partial differential equations. Before I joined the mathematics department, I worked a couple of years as a numerical solver developer at Comsol AB.
I got my Ph.D in computational mathematics in 2013 under the supervision of Prof. Jan Nordström, with the thesis "Stable and High-Order Finite Difference Methods for Multiphysics Flow Problems".
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My current research is focused on deep learning and deep artificial neural networks for partial differential equations. We use tools from machine learning to solve inverse problems for PDEs, for example coefficient estimation and optimal control, as well as in the discovery of new PDE models from observed data.
Besides deep learning and neural networks, I have an interest in numerical methods for PDEs. In particular stability of finite difference methods, and more recently also algebraic multigrid.
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