A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: Comparison with finite element method

Computer Methods in Applied Mechanics and Engineering, vol. 401, pp. 115616

Authors 4

1. Shahed Rezaei corresponding

   Technische Universität Darmstadt

   Affiliation as printed

   Mechanics of Functional Materials Division, Institute of Materials Science, Technical University of Darmstadt, Otto-Berndt-Str. 3, D-64287 Darmstadt, Germany

2. Ahmad Moeineddin Aachen Institute of Applied Mechanics

   RWTH Aachen University

   Affiliation as printed

   Institute of Applied Mechanics, RWTH Aachen University, Mies-van-der-Rohe-Str. 1, D-52074 Aachen, Germany

3. Bai‐Xiang Xu

   Technische Universität Darmstadt

   Affiliation as printed

   Mechanics of Functional Materials Division, Institute of Materials Science, Technical University of Darmstadt, Otto-Berndt-Str. 3, D-64287 Darmstadt, Germany

4. Stefanie Reese Aachen Institute of Applied Mechanics

   RWTH Aachen University

   Affiliation as printed

   Institute of Applied Mechanics, RWTH Aachen University, Mies-van-der-Rohe-Str. 1, D-52074 Aachen, Germany