%0 Generic %1 mitusch2021hybrid %A Mitusch, Sebastian K. %A Funke, Simon W. %A Kuchta, Miroslav %D 2021 %K PDE linear_algebra neural_networks numerical_methods %T Hybrid FEM-NN models: Combining artificial neural networks with the finite element method %U http://arxiv.org/abs/2101.00962 %X We present a methodology combining neural networks with physical principle constraints in the form of partial differential equations (PDEs). The approach allows to train neural networks while respecting the PDEs as a strong constraint in the optimisation as apposed to making them part of the loss function. The resulting models are discretised in space by the finite element method (FEM). The methodology applies to both stationary and transient as well as linear/nonlinear PDEs. We describe how the methodology can be implemented as an extension of the existing FEM framework FEniCS and its algorithmic differentiation tool dolfin-adjoint. Through series of examples we demonstrate capabilities of the approach to recover coefficients and missing PDE operators from observations. Further, the proposed method is compared with alternative methodologies, namely, physics informed neural networks and standard PDE-constrained optimisation. Finally, we demonstrate the method on a complex cardiac cell model problem using deep neural networks.

@misc{mitusch2021hybrid, abstract = {We present a methodology combining neural networks with physical principle constraints in the form of partial differential equations (PDEs). The approach allows to train neural networks while respecting the PDEs as a strong constraint in the optimisation as apposed to making them part of the loss function. The resulting models are discretised in space by the finite element method (FEM). The methodology applies to both stationary and transient as well as linear/nonlinear PDEs. We describe how the methodology can be implemented as an extension of the existing FEM framework FEniCS and its algorithmic differentiation tool dolfin-adjoint. Through series of examples we demonstrate capabilities of the approach to recover coefficients and missing PDE operators from observations. Further, the proposed method is compared with alternative methodologies, namely, physics informed neural networks and standard PDE-constrained optimisation. Finally, we demonstrate the method on a complex cardiac cell model problem using deep neural networks.}, added-at = {2021-02-12T22:51:49.000+0100}, author = {Mitusch, Sebastian K. and Funke, Simon W. and Kuchta, Miroslav}, biburl = {https://www.bibsonomy.org/bibtex/2f3a68e966da9181528213bfcd802cc49/peter.ralph}, interhash = {fa571b5d20fceda429f772bef0cce482}, intrahash = {f3a68e966da9181528213bfcd802cc49}, keywords = {PDE linear_algebra neural_networks numerical_methods}, note = {cite arxiv:2101.00962}, timestamp = {2021-02-12T22:51:49.000+0100}, title = {Hybrid {FEM-NN} models: {Combining} artificial neural networks with the finite element method}, url = {http://arxiv.org/abs/2101.00962}, year = 2021 }