Silvia Ferrari

Image of Silvia Ferrari

Adjunct Professor of Mechanical Engineering and Materials Science

Professor Ferrari's research aims at providing intelligent control systems with a higher degree of mathematical structure to guide their application and improve reliability. Decision-making processes are automated based on concepts drawn from control theory and the life sciences. Recent efforts have focused on the development of reconfigurable controllers implementing neural networks with procedural long-term memories. Full-scale simulations show that these controllers are capable of learning from new and unmodeled aircraft dynamics in real time, improving performance and even preventing loss of control in the event of control failures, nonlinear and near-stall dynamics, and parameter variations. New optimal control problems and methods based on computational geometry are being investigated to improve the effectiveness of integrated surveillance systems by networks of autonomous vehicles, such as, underwater gliders and ground robots.

Appointments and Affiliations
  • Adjunct Professor of Mechanical Engineering and Materials Science
  • Faculty Network Member of the Duke Institute for Brain Sciences
Contact Information:

  • 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

Representative Publications: (More Publications)
    • C. Cai and S. Ferrari, Information-Driven Sensor Path Planning by Approximate Cell Decomposition, IEEE Transaction on Systems, Man, and Cybernetics - Part B (Submitted, in revision).
    • S. Ferrari, Multi-Objective Algebraic Synthesis of Neural Control Systems by Implicit Model Following, IEEE Transactions on Neural Networks (Submitted, in press).
    • Ferrari S., Steck J.E. and Chandramohan R., Adaptive Feedback Control by Constrained Approximate Dynamic Programming, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, vol 38 no. 4 (August 2008), pp. 982-987.
    • K. Baumgartner and S. Ferrari, A geometric transversal approach to analyzing track coverage in sensor networks, Ieee Transactions On Computers, vol 57 no. 8 (2008), pp. 1113 -- 1128.
    • Baumgartner, K.C., and Ferrari S., Constructing Bayesian Networks for Criminal Profiling from Limited Data, Knowledge-Based Systems (Accepted, in press) (Available online at: .
    • Ferrari, S., and Jensenius M., A Constrained Optimization Approach to Preserving Prior Knowledge During Incremental Training, IEEE Transactions on Neural Networks, vol 19 no. 6 (June 2008).
    • Ferrari, Silvia and Vaghi, Alberto, Demining sensor modeling and feature-level fusion by bayesian networks, IEEE Sensors Journal, vol 6 no. 2 (2006), pp. 471 - 483 [JSEN.2006.870162] [abs].
    • Ferrari, Silvia and Stengel, Robert F., Smooth function approximation using neural networks, IEEE Transactions on Neural Networks, vol 16 no. 1 (2005), pp. 24 - 38 [TNN.2004.836233] [abs].
    • S. Ferrari and R. F. Stengel, Online adaptive critic flight control, Journal Of Guidance Control And Dynamics, vol 27 no. 5 (2004), pp. 777 -- 786.
    • Ferrari, Silvia and Stengel, Robert F., Classical/neural synthesis of nonlinear control systems, Journal of Guidance, Control, and Dynamics, vol 25 no. 3 (2002), pp. 442 - 448 [abs].