Applied Intelligent Systems

Applied Intelligent Systems Laboratory (AISL) is a research unit at the School of Nuclear Engineering at Purdue University that performs active research dedicated towards muon imaging with special emphasis in nuclear applications and the application of innovative problem solving techniques to modeling, simulation and control of complex systems. These techniques include novel signal processing and machine learning methodologies such as artificial neural networks, fuzzy logic, genetic algorithms and evolutionary computing, Gaussian processes, wavelet analysis, Hilbert-Huang transform, expert systems and advanced signal/image processing tools. The research team has state-of-the-art hardware and software facilities, including a high performance computing grid already in place to enable the development of a computing architecture to perform large-scale high-fidelity simulations and analyze experimental data, and significant experience with industrial and safeguards applications.

Competitively funded projects, among others, include: Creation of a Geant4 Muon Tomography Package for Imaging of Nuclear Fuel in Dry Cask Storage (funded by DOE), Smart Data Embedding and Inverse Solution Algorithms, Modeling and Simulation for Nonproliferation (sponsored by NNSA), Intelligent Management of the Electric Power Grid (sponsored by DOD and EPRI), Trend Identification in Nuclear Reactor Control (sponsored by ANL), Intelligent Flowmeter Diagnostics (sponsored by Emerson Electric) and, Hazardous Material Identification (sponsored by Crane Naval Systems). AISL participates in the $25 million grant from the National Nuclear Security Administration (NNSA) consortium for research and development in enabling capabilities for nuclear nonproliferation (CNEC). Interdepartmental laboratories and facilities are available if necessary, including fully equipped radiation laboratories, a research reactor and gamma irradiation facilities.

AISL researchers and students are recipients of several awards, including best papers, NNSA fellowships and outstanding research and teaching awards. Highly motivated students with strong mathematical background are encouraged to apply to join the group.

Contact: Dr. Tsoukalas

Dr. Choi

Dr. Alamaniotis

Laboratory webpage: