19 citations as recorded by crossref.

1. On some applications of Galilean electrodynamics of moving bodies M. de Montigny & G. Rousseaux https://doi.org/10.1119/1.2772289
2. Multiple traces formulation and semi-implicit scheme for modelling biological cells under electrical stimulation F. Henríquez & C. Jerez-Hanckes https://doi.org/10.1051/m2an/2018019
3. Neural Tissue Degeneration in Rosenthal’s Canal and Its Impact on Electrical Stimulation of the Auditory Nerve by Cochlear Implants: An Image-Based Modeling Study K. Sriperumbudur et al. https://doi.org/10.3390/ijms21228511
4. Numerical Simulations as Means for Tailoring Electrically Conductive Hydrogels towards Cartilage Tissue Engineering by Electrical Stimulation J. Zimmermann et al. https://doi.org/10.3390/molecules25204750
5. Experimental and numerical methods to ensure comprehensible and replicable alternating current electrical stimulation experiments J. Zimmermann et al. https://doi.org/10.1016/j.bioelechem.2023.108395
6. Understanding the impact of modiolus porosity on stimulation of spiral ganglion neurons by cochlear implants K. Sriperumbudur et al. https://doi.org/10.1038/s41598-024-59347-2
7. Establishment of a Numerical Model to Design an Electro-Stimulating System for a Porcine Mandibular Critical Size Defect H. Raben et al. https://doi.org/10.3390/app9102160
8. Influence of Neuronal Morphology on the Shape of Extracellular Recordings With Microelectrode Arrays: A Finite Element Analysis R. Bestel et al. https://doi.org/10.1109/TBME.2020.3026635
9. Numerical study on the effect of capacitively coupled electrical stimulation on biological cells considering model uncertainties J. Zimmermann et al. https://doi.org/10.1038/s41598-022-08279-w
10. Contributions of deep learning to automated numerical modelling of the interaction of electric fields and cartilage tissue based on 3D images V. Che et al. https://doi.org/10.3389/fbioe.2023.1225495
11. Evaluating the electrical stimulation of bone cells based on an induced transmembrane potential model and intracellular calcium levels M. Bielfeldt et al. https://doi.org/10.1177/20417314251414697
12. Addressing Model Uncertainties in Finite Element Simulation of Electrically Stimulated Implants for Critical-Size Mandibular Defects H. Raben et al. https://doi.org/10.1109/TBME.2024.3408076
13. Realistic image-based simulations of multicellular systems exposed to electric fields for applications in impedance sensing and electrical stimulation J. Zimmermann et al. https://doi.org/10.1016/j.sbsr.2025.100950
14. Resistor–capacitor modeling of the cell membrane: A multiphysics analysis C. Brosseau & E. Sabri https://doi.org/10.1063/5.0033608
15. Using a Digital Twin of an Electrical Stimulation Device to Monitor and Control the Electrical Stimulation of Cells in vitro J. Zimmermann et al. https://doi.org/10.3389/fbioe.2021.765516
16. Electrical stimulation for cartilage tissue engineering - A critical review from an engineer's perspective J. Zimmermann et al. https://doi.org/10.1016/j.heliyon.2024.e38112
17. Effect of Tissue Heterogeneity on the Transmembrane Potential of Type-1 Spiral Ganglion Neurons: A Simulation Study K. Sriperumbudur et al. https://doi.org/10.1109/TBME.2017.2700361
18. Deriving Models of Cartilaginous Cells From Confocal Fluorescence Microscopy Images to Estimate Dielectric Properties V. Che et al. https://doi.org/10.1109/TMAG.2023.3307009
19. Boundary integral formulation and semi-implicit scheme coupling for modeling cells under electrical stimulation F. Henríquez et al. https://doi.org/10.1007/s00211-016-0835-9