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S. Mitusch, S. W. Funke and M. Kuchta

Hybrid FEM-NN models: Combining artificial neural networks with the finite element method

Journal of Computational Physics (2021).
Journal Article
2021
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S. Mitusch, S. W. Funke and J. S. Dokken

dolfin-adjoint 2018.1: automated adjoints for FEniCS and Firedrake

Journal of Open Source Software 4, no. 38 (2019): 1292.
Journal Article
2019
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2021

Journal articles

S. Mitusch, S. W. Funke and M. Kuchta

Hybrid FEM-NN models: Combining artificial neural networks with the finite element method

Journal of Computational Physics (2021).
Status: Submitted
2019

Journal articles

S. Mitusch, S. W. Funke and J. S. Dokken

dolfin-adjoint 2018.1: automated adjoints for FEniCS and Firedrake

Journal of Open Source Software 4, no. 38 (2019): 1292.
Status: Published

Posters

S. Mitusch and S. W. Funke

Algorithmic differentiation for coupled FEniCS and PyTorch models

ICCOPT, Berlin, Germany, 2019.
Status: Published

Talks, invited

S. W. Funke, J. S. Dokken and S. Mitusch

Algorithmic differentation for shape derivatives with PDE constraints

In Berlin, Germany, 2019.
Status: Published

Talks, contributed

S. Mitusch, S. W. Funke and J. S. Dokken

Recent developments in dolfin-adjoint

In FEniCS'19, Washington DC, USA, 2019.
Status: Published