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

B. Sayyarrodsari | Zendy; Carl A. Schweiger | Zendy; Eric Hartman | Zendy

Open Access

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

Author(s) -

B. Sayyarrodsari,

Carl A. Schweiger,

Eric Hartman

Publication year - 2007

Publication title -

osti oai (u.s. department of energy office of scientific and technical information)

Language(s) - English

Resource type - Reports

DOI - 10.2172/917186

Subject(s) - nonlinear system , computer science , artificial neural network , parametric statistics , mathematical model , fidelity , system dynamics , control engineering , mathematical optimization , artificial intelligence , engineering , mathematics , physics , telecommunications , statistics , quantum mechanics

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

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