Application of artificial neural networks for the prediction of interface mechanics: a study on grain boundary constitutive behavior. - Mauricio Fernández; Shahed Rezaei; Jaber Rezaei Mianroodi; Felix Fritzen; Stefanie Reese (Adv. Model. Simul. Eng. Sci., 2020)

Lossless Multi-Scale Constitutive Elastic Relations with Artificial Intelligence. - Jaber Rezaei Mianroodi; Shahed Rezaei; Nima H. Siboni; Bai-Xiang Xu; Dierk Raabe (CoRR, 2021)

An anisotropic cohesive fracture model: advantages and limitations of length-scale insensitive phase-field damage models. - Shahed Rezaei; Ali Harandi; Tim Brepols; Stefanie Reese (CoRR, 2021)

A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method. - Shahed Rezaei; Ali Harandi; Ahmad Moeineddin; Bai-Xiang Xu; Stefanie Reese (CoRR, 2022)

AI enhanced finite element multiscale modelling and structural uncertainty analysis of a functionally graded porous beam. - Da Chen; Nima Emami; Shahed Rezaei; Philipp L. Rosendahl; Bai-Xiang Xu; Jens Schneider; Kang Gao; Jie Yang (CoRR, 2022)

Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains. - Ali Harandi; Ahmad Moeineddin; Michael Kaliske; Stefanie Reese; Shahed Rezaei (CoRR, 2023)

Learning solution of nonlinear constitutive material models using physics-informed neural networks: COMM-PINN. - Shahed Rezaei; Ahmad Moeineddin; Ali Harandi (CoRR, 2023)

Comparative analysis of numerical approaches for fracture simulation in multiphase materials containing interfaces: Unveiling the potential of microstructural design to enhance fracture properties. - Rasoul Najafi Koopas; Shahed Rezaei; Natalie Rauter; Richard Ostwald; Rolf Lammering (CoRR, 2023)

Integration of physics-informed operator learning and finite element method for parametric learning of partial differential equations. - Shahed Rezaei; Ahmad Moeineddin; Michael Kaliske; Markus Apel (CoRR, 2024)

A finite operator learning technique for mapping the elastic properties of microstructures to their mechanical deformations. - Shahed Rezaei; Shirko Faroughi; Mahdi Asgharzadeh; Ali Harandi; Gottfried Laschet; Stefanie Reese; Markus Apel (CoRR, 2024)

Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric space. - Rasoul Najafi Koopas; Shahed Rezaei; Natalie Rauter; Richard Ostwald; Rolf Lammering (CoRR, 2024)

Finite Operator Learning: Bridging Neural Operators and Numerical Methods for Efficient Parametric Solution and Optimization of PDEs. - Shahed Rezaei; Reza Najian Asl; Kianoosh Taghikhani; Ahmad Moeineddin; Michael Kaliske; Markus Apel (CoRR, 2024)

A spatiotemporal deep learning framework for prediction of crack dynamics in heterogeneous solids: efficient mapping of concrete microstructures to its fracture properties. - Rasoul Najafi Koopas; Shahed Rezaei; Natalie Rauter; Richard Ostwald; Rolf Lammering (CoRR, 2024)

A Spectral-based Physics-informed Finite Operator Learning for Prediction of Mechanical Behavior of Microstructures. - Ali Harandi; Hooman Danesh; Kevin Linka; Stefanie Reese; Shahed Rezaei (CoRR, 2024)

A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains. - Yusuke Yamazaki; Ali Harandi; Mayu Muramatsu; Alexandre Viardin; Markus Apel; Tim Brepols; Stefanie Reese; Shahed Rezaei (Eng. Comput., 2025)

A Physics-Informed Meta-Learning Framework for the Continuous Solution of Parametric PDEs on Arbitrary Geometries. - Reza Najian Asl; Yusuke Yamazaki; Kianoosh Taghikhani; Mayu Muramatsu; Markus Apel; Shahed Rezaei (CoRR, 2025)