Accelerated Simulation of a Neuronal Population via Mathematical Model Order Reduction

Author(s):  
Mikko Lehtimaki ◽  
Ippa Seppala ◽  
Lassi Paunonen ◽  
Marja-Leena Linne
2021 ◽  
Vol 2021 (3) ◽  
pp. 4644-4651
Author(s):  
S. Schroeder ◽  
◽  
S. Ihlenfeldt ◽  
L. Penter ◽  
◽  
...  

To control the thermal behavior of machine tools, numerous measures are used today. With the help of thermal machine models, well-founded predictions can be made for the selection and design of these measures. For creation and simulation of thermo-elastic models of structural components and a high effort is still necessary. In the paper, a procedure is presented to reduce this effort. A combination of existing methods and new approaches is used. This includes methods of mathematical model order reduction to reduce computing costs and robust mesh algorithms that process even slightly defective geometries.


Today’s world is a world of simulation. Now a days, every product is first designed in virtual domain and then tested for actual implementation. To be able to perform such accurate virtual domain analysis, an accurate mathematical model is needed to be designed in first place. Specifically, in the field of dynamic analysis, so as to continuously monitor the system, the requirement a high-fidelity simulation models in all industries is rising rapidly and this has now become an important part of modern simulation strategies. FEA simulation software’s nowadays could provide very accurate results but they cannot be used directly for dynamic simulations where the environment is continuously changing (input forces, random vibrations etc.). Therefore, this paper deals with design of a mathematical model of a beam to overcome the above stated issue. The technique so used is Model Order Reduction. This method develops an efficient reduced model by reducing the degrees of freedom and also preserving a characteristic behavior of the system. The methodology deals with extracting the mass, and stiffness matrices from FEA simulation software, reducing their size (order), building a second order system using reduced sizes of mass and stiffness, analyzing mode shapes vectors and nodes for input force applications, and generating a state space model of the system.


Author(s):  
Vladimir Lantsov ◽  
A. Papulina

The new algorithm of solving harmonic balance equations which used in electronic CAD systems is presented. The new algorithm is based on implementation to harmonic balance equations the ideas of model order reduction methods. This algorithm allows significantly reduce the size of memory for storing of model equations and reduce of computational costs.


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