A nonlocal physics-informed deep learning framework using the peridynamic differential operator. - Ehsan Haghighat; Ali Can Bekar; Erdogan Madenci; Ruben Juanes (CoRR, 2020)

Deep learning for solution and inversion of structural mechanics and vibrations. - Ehsan Haghighat; Ali Can Bekar; Erdogan Madenci; Ruben Juanes (CoRR, 2021)

Peridynamics enabled learning partial differential equations. - Ali Can Bekar; Erdogan Madenci (J. Comput. Phys., 2021)

An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator. - A. Mavi; Ali Can Bekar; Ehsan Haghighat; Erdogan Madenci (CoRR, 2022)

Nonlocal Physics Informed Machine Learning Algorithms Using Peridynamic Differential Operator. - Ali Can Bekar (2023)

Accelerating the discovery of steady-states of planetary interior dynamics with machine learning. - Siddhant Agarwal; Nicola Tosi; Christian Hüttig; David S. Greenberg; Ali Can Bekar (CoRR, 2024)

Multiphysics discovery with moving boundaries using Ensemble SINDy and peridynamic differential operator. - Ali Can Bekar; Ehsan Haghighat; Erdogan Madenci (Eng. Comput., 2025)

Physics-based machine learning for mantle convection simulations. - Siddhant Agarwal; Ali Can Bekar; Christian Hüttig; David S. Greenberg; Nicola Tosi (CoRR, 2025)