Employing in-vivo molecular imaging in simulating and validating tumor growth. - Eleftheria Tzamali; Rosy Favicchio; Alexandros Roniotis; Giorgios Tzedakis; Giorgos Grekas; Jorge Ripoll; Kostas Marias; Giannis Zacharakis; Vangelis Sakkalis (EMBC, 2013)

The importance of grid size and boundary conditions in discrete tumor growth modeling. - Giorgios Tzedakis; Giorgos Grekas; Eleftheria Tzamali; Kostas Marias; Vangelis Sakkalis (IARWISOI, 2014)

Approximations of cell-induced phase transitions in fibrous biomaterials: Γ-convergence analysis. - Georgios Grekas; Konstantinos Koumatos; Charalambos G. Makridakis; Phoebus Rosakis (CoRR, 2019)

Approximations of Energy Minimization in Cell-Induced Phase Transitions of Fibrous Biomaterials: $ Gamma$-Convergence Analysis. - Georgios Grekas; Konstantinos Koumatos; Charalambos G. Makridakis; Phoebus Rosakis (SIAM J. Numer. Anal., 2022)

Deep Ritz - Finite Element methods: Neural Network Methods trained with Finite Elements. - Georgios Grekas; Charalambos G. Makridakis (CoRR, 2024)

Compressive instabilities enable cell-induced extreme densification patterns in the fibrous extracellular matrix: Discrete model predictions. - Chrysovalantou Kalaitzidou; Georgios Grekas; Andreas Zilian; Charalambos G. Makridakis; Phoebus Rosakis (PLoS Comput. Biol., 2024)

A class of discontinuous Galerkin methods for nonlinear variational problems. - Georgios Grekas; Konstantinos Koumatos; Charalambos G. Makridakis; Andreas Vikelis (Math. Comput., 2025)

Convergence of Discontinuous Galerkin Methods for Quasiconvex and Relaxed Variational Problems. - Georgios Grekas; Konstantinos Koumatos; Charalambos G. Makridakis; Andreas P. Vikelis (CoRR, 2025)

PINN-DG: Residual neural network methods trained with Finite Elements. - Georgios Grekas; Charalambos G. Makridakis; Tristan Pryer (CoRR, 2025)