Silvia Ferrari

Silvia Ferrari

Adjunct Professor in the Department of Mechanical Engineering and Materials Science

Appointments and Affiliations

  • Adjunct Professor in the Department of Mechanical Engineering and Materials Science
  • Faculty Network Member of the Duke Institute for Brain Sciences

Contact Information

  • Office Location: 144 Hudson Hall, Box 90300, Durham, NC 27708
  • Email Address:


  • Ph.D. Princeton University, 2002
  • M.A. Princeton University, 1999
  • B.S. Embry-Riddle Aeronautical University, 1997

Research Interests

Design and analysis of methods and algorithms for learning and computational intelligence. Theory and approximation properties of network models, such as neural and probabilistic networks, for the purpose of enhancing their learning abilities and improving reliability. Approximate dynamic programming and optimal control techniques, with applications in adaptive flight control and mobile sensor networks. Application of expert systems and systems theory to psychological and cognitive modeling from data.


neural networks
Bayesian networks
Smart Technology

Courses Taught

  • ME 344L: Control of Dynamic Systems
  • ME 392: Undergraduate Projects in Mechanical Engineering
  • ME 491: Special Projects in Mechanical Engineering
  • ME 492: Special Projects in Mechanical Engineering
  • ME 555: Advanced Topics in Mechanical Engineering
  • ME 759: Special Readings in Mechanical Engineering

In the News

Representative Publications

  • Rudd, K; Ferrari, S, A constrained integration (CINT) approach to solving partial differential equations using artificial neural networks, Neurocomputing, vol 155 (2015), pp. 277-285 [10.1016/j.neucom.2014.11.058] [abs].
  • Wei, H; Ferrari, S, A Geometric Transversals Approach to Analyzing the Probability of Track Detection for Maneuvering Targets, IEEE Transactions on Computers, vol 63 no. 11 (2014), pp. 2633-2646 [10.1109/TC.2013.43] [abs].
  • Rudd, K; Albertson, JD; Ferrari, S, Optimal root profiles in water-limited ecosystems, Advances in Water Resources, vol 71 (2014), pp. 16-22 [10.1016/j.advwatres.2014.04.021] [abs].
  • Lu, W; Zhang, G; Ferrari, S, An Information Potential Approach to Integrated Sensor Path Planning and Control, IEEE Transactions on Robotics, vol 30 no. 4 (2014), pp. 919-934 [10.1109/TRO.2014.2312812] [abs].
  • Rudd, K; Di Muro, G; Ferrari, S, A constrained backpropagation approach for the adaptive solution of partial differential equations., IEEE Transactions on Neural Networks and Learning Systems, vol 25 no. 3 (2014), pp. 571-584 [10.1109/tnnls.2013.2277601] [abs].