Simulation of large systems with neural networks

Conference · Thu Sep 01 04:00:00 EDT 1994

OSTI ID:10183240

Paez, T L

Artificial neural networks (ANNs) have been shown capable of simulating the behavior of complex, nonlinear, systems, including structural systems. Under certain circumstances, it is desirable to simulate structures that are analyzed with the finite element method. For example, when we perform a probabilistic analysis with the Monte Carlo method, we usually perform numerous (hundreds or thousands of) repetitions of a response simulation with different input and system parameters to estimate the chance of specific response behaviors. In such applications, efficiency in computation of response is critical, and response simulation with ANNs can be valuable. However, finite element analyses of complex systems involve the use of models with tens or hundreds of thousands of degrees of freedom, and ANNs are practically limited to simulations that involve far fewer variables. This paper develops a technique for reducing the amount of information required to characterize the response of a general structure. We show how the reduced information can be used to train a recurrent ANN. Then the trained ANN can be used to simulate the reduced behavior of the original system, and the reduction transformation can be inverted to provide a simulation of the original system. A numerical example is presented.

• Research Organization: Sandia National Labs., Albuquerque, NM (United States)
• Sponsoring Organization: USDOE, Washington, DC (United States)
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 10183240
• Report Number(s): SAND--94-2314C; CONF-9410187--2; ON: DE94018898; BR: GB0103012
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
990200
COMPUTERIZED SIMULATION
FINITE ELEMENT METHOD
MATHEMATICS AND COMPUTERS
MONTE CARLO METHOD
NEURAL NETWORKS
RUNGE-KUTTA METHOD
STRUCTURAL MODELS
SYSTEMS ANALYSIS