Development of PUNDA (Parametric Universal Nonlinear Dynamics Approximator) Models for Self-Validating Knowledge-Guided Modelling of Nonlinear Processes in Particle Accelerators \& Industry

[摘要] The difficult problems being tackled in the accelerator community are those that are nonlinear, substantially unmodeled, and vary over time. Such problems are ideal candidates for model-based optimization and control if representative models of the problem can be developed that capture the necessary mathematical relations and remain valid throughout the operation region of the system, and through variations in system dynamics. The goal of this proposal is to develop the methodology and the algorithms for building high-fidelity mathematical representations of complex nonlinear systems via constrained training of combined first-principles and neural network models.

[发布日期] 2007-10-07

[学科分类] 核物理和高能物理

[关键词] Complex System Modelling;Combined Neural Networks and First-Principles Models;Model-based Optimization and Control