160 citations · 160 across the 1 of their papers we have counts for
3 papers
stat.ML2018★ 160 cited
Data-driven discovery of PDEs in complex datasets
Jens Berg, Kaj Nyström
Many processes in science and engineering can be described by partial differential equations (PDEs). Traditionally, PDEs are derived by considering first principles of physics to d…
stat.ML2017
Neural network augmented inverse problems for PDEs
Jens Berg, Kaj Nyström
In this paper we show how to augment classical methods for inverse problems with artificial neural networks. The neural network acts as a prior for the coefficient to be estimated…
stat.ML2017
A unified deep artificial neural network approach to partial differential equations in complex geometries
Jens Berg, Kaj Nyström
In this paper we use deep feedforward artificial neural networks to approximate solutions to partial differential equations in complex geometries. We show how to modify the backpro…