23 citations as recorded by crossref.

1. Simulating the thermorheological evolution of channel-contained lava: FLOWGO and its implementation in EXCEL A. Harris et al. https://doi.org/10.1144/SP426.9
2. Assessing Impact to Infrastructures Due to Tephra Fallout From Öræfajökull Volcano (Iceland) by Using a Scenario-Based Approach and a Numerical Model S. Barsotti et al. https://doi.org/10.3389/feart.2018.00196
3. Probabilistic approach to modeling lava flow inundation: a lava flow hazard assessment for a nuclear facility in Armenia L. Connor et al. https://doi.org/10.1186/2191-5040-1-3
4. CNN‐based ultrafast solver of stiff ODEs and PDEs for enabling realtime Computational Engineering L. Klinkenbusch et al. https://doi.org/10.1108/03321641111133235
5. Lava flow hazard prediction and monitoring with UAS: a case study from the 2014–2015 Pāhoa lava flow crisis, Hawai‘i N. Turner et al. https://doi.org/10.1186/s13617-017-0068-3
6. Lava-flow hazard on the SE flank of Mt. Etna (Southern Italy) G. Crisci et al. https://doi.org/10.1016/j.jvolgeores.2008.01.041
7. Numerical simulation of basaltic lava flows in the Auckland Volcanic Field, New Zealand—implication for volcanic hazard assessment G. Kereszturi et al. https://doi.org/10.1007/s00445-014-0879-6
8. Uncertainties in lava flow hazard maps derived from numerical simulations: The case study of Mount Etna S. Tarquini & M. Favalli https://doi.org/10.1016/j.jvolgeores.2013.04.017
9. Risk evaluation, detection and simulation during effusive eruption disasters A. Harris et al. https://doi.org/10.1144/SP426.29
10. A Novel General and Robust Method Based on NAOP for Solving Nonlinear Ordinary Differential Equations and Partial Differential Equations by Cellular Neural Networks J. Chamberlain Chedjou & K. Kyamakya https://doi.org/10.1115/1.4023241
11. Chemical, Textural and Thermal Analyses of Local Interactions Between Lava Flow and a Tree – Case Study From Pāhoa, Hawai’i J. Biren et al. https://doi.org/10.3389/feart.2020.00233
12. Modeling of the 2001 lava flow at Etna volcano by a Cellular Automata approach A. Vicari et al. https://doi.org/10.1016/j.envsoft.2006.10.005
13. How Nonlinear Science Leads to Continuous Innovation in an Academic and Productive High-Tech Community L. Fortuna et al. https://doi.org/10.33581/1561-4085-2022-25-1-1-12
14. Machine Learning for parameter estimation from Cellular Nonlinear Networks simulations G. Spina et al. https://doi.org/10.1016/j.ifacol.2025.12.493
15. Hybrid modeling of multiphysical processes for particle‐based volcano animation S. Zhang et al. https://doi.org/10.1002/cav.1758
16. Damage assessment for the 2018 lower East Rift Zone lava flows of Kīlauea volcano, Hawaiʻi E. Meredith et al. https://doi.org/10.1007/s00445-022-01568-2
17. Forecasting lava flow hazards during the 2006 Etna eruption: Using the MAGFLOW cellular automata model A. Herault et al. https://doi.org/10.1016/j.cageo.2007.10.008
18. Simulations of the 2004 lava flow at Etna volcano using the magflow cellular automata model C. Del Negro et al. https://doi.org/10.1007/s00445-007-0168-8
19. An emergent strategy for volcano hazard assessment: From thermal satellite monitoring to lava flow modeling G. Ganci et al. https://doi.org/10.1016/j.rse.2011.12.021
20. MAGFLOW: a physics-based model for the dynamics of lava-flow emplacement A. Cappello et al. https://doi.org/10.1144/SP426.16
21. How shear helps lava to flow A. Harris et al. https://doi.org/10.1130/G47110.1
22. Conclusion: recommendations and findings of the RED SEED working group A. Harris et al. https://doi.org/10.1144/SP426.11
23. Hazard assessment at Mount Etna using a hybrid lava flow inundation model and satellite-based land classification A. Harris et al. https://doi.org/10.1007/s11069-010-9709-0