Staff profile

I've joined Durham in March 2015. Prior to that, I was a Senior Research Associate at the Centre for Mathematical Sciences, University of Cambridge.

My web page can be found here.

• 2026: EPSRC panel member: Mathematical Sciences Prioritisation Panel November 2026:
• 2026 - 2028: Member, Royal Society's Research Grants Committee (Physical Sciences): From 01/01/2026 until 31/12/2028.
• 2024 - 2027: Royal Society International Exchanges Committee:
• 2024 - 2026: EPSRC Researcher in Residence, CPI National Biologics Manufacturing Centre, Darlington:
• 2018: Recognised by the EPSRC Chief Executive for peer review in 2016/17, ranking in the top 7% of Peer Review College members: Recognition that I have achieved a ranking in the top 7% of College members for participating in peer review activities during the academic year (2016/17).
• 2016: Fellow of the Higher Education Academy:
• 2015 - 2017: EPSRC panel member: Mathematics in Healthcare (2015), EPSRC–KETEP Smart Grids (2016), Mathematics Prioritisation Panel (2017):
• 2015: Research visit, MIT Laboratory for Information and Decision Systems, hosted by Prof. Munther Dahleh:
• 2012 - 2027: Member, EPSRC Peer Review College:

Conference Paper

• Collective Effects and Performance of Algorithmic Electric Vehicle Charging Strategies
  
  Gardlo, M., Buzna, Ľ., Carvalho, R., Gibbens, R., & Kelly, F. (2018). Collective Effects and Performance of Algorithmic Electric Vehicle Charging Strategies. In 2018 IEEE Workshop on Complexity in Engineering (COMPENG), 10-12 October 2018, Florence. (pp. 1-7). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/compeng.2018.8536246
• Congestion dependencies in the European gas pipeline network during crises
  
  Buzna, L., Carvalho, R., Bono, F., Masera, M., & Arrowsmith, D. (2014). Congestion dependencies in the European gas pipeline network during crises. In M. Rosas-Casals & A. Grau (Eds.), 2014 Workshop on Complexity in Engineering (COMPENG) : June 16-17, 2014, Faculty of Mathematics and Statistics, Barcelona, Spain. (pp. 1-5). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/compeng.2014.6994681

Journal Article

• Automating the discovery of partial differential equations in dynamical systems
  
  Li, W., & Carvalho, R. (2024). Automating the discovery of partial differential equations in dynamical systems. Machine Learning: Science and Technology, 5(3), Article 035046. https://doi.org/10.1088/2632-2153/ad682f
• Automatically discovering ordinary differential equations from data with sparse regression
  
  Egan, K., Li, W., & Carvalho, R. (2024). Automatically discovering ordinary differential equations from data with sparse regression. Communications Physics, 7(1), Article 20. https://doi.org/10.1038/s42005-023-01516-2
• Analysis of Energy Consumption at Slow Charging Infrastructure for Electric Vehicles
  
  Straka, M., Carvalho, R., Poel, G. V. D., & Buzna, L. (2021). Analysis of Energy Consumption at Slow Charging Infrastructure for Electric Vehicles. IEEE Access, 9. https://doi.org/10.1109/access.2021.3071180
• Controlling congestion on complex networks: fairness, efficiency and network structure
  
  Buzna, Ľ., & Carvalho, R. (2017). Controlling congestion on complex networks: fairness, efficiency and network structure. Scientific Reports, 7(1), Article 9152. https://doi.org/10.1038/s41598-017-09524-3
• Critical behaviour in charging of electric vehicles
  
  Carvalho, R., Buzna, L., Gibbens, R., & Kelly, F. (2015). Critical behaviour in charging of electric vehicles. New Journal of Physics, 17(9), Article 095001. https://doi.org/10.1088/1367-2630/17/9/095001
• Resilience of Natural Gas Networks during Conflicts, Crises and Disruptions
  
  Carvalho, R., Buzna, L., Bono, F., Masera, M., Arrowsmith, D. K., & Helbing, D. (2014). Resilience of Natural Gas Networks during Conflicts, Crises and Disruptions. PLoS ONE, 9(3), Article e90265. https://doi.org/10.1371/journal.pone.0090265

Report

• Automatically identifying ordinary differential equations from data
  
  Egan, K., Li, W., & Carvalho, R. (2023). Automatically identifying ordinary differential equations from data. Durham University. https://doi.org/10.48550/arXiv.2304.11182

Working Paper

• py-ARGOS: A Python package for automatic, statistically principled discovery of ordinary differential equations from data
  
  Zhang, Y., & Carvalho, R. (2026). py-ARGOS: A Python package for automatic, statistically principled discovery of ordinary differential equations from data. arXiv. https://doi.org/10.48550/arXiv.2610.34458
• Fast and principled equation discovery from chaos to climate
  
  Carvalho, R., & Zhang, Y. (2026). Fast and principled equation discovery from chaos to climate. arXiv. https://doi.org/10.48550/arXiv.2604.11929
• Explaining the distribution of energy consumption at slow charging infrastructure for electric vehicles from socio-economic data
  
  Strakaa, M., Carvalho, R., van der Poel, G., & Buzna, Ľ. (2020). Explaining the distribution of energy consumption at slow charginginfrastructure for electric vehicles from socio-economic data.