在科学和工业中加强AMS系统仿真的CICT IOU参考框架

R. Fiorini
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引用次数: 3

摘要

为了发展弹性和抗脆弱的应用和模拟,我们需要更强的生物和物理系统关联;换句话说,我们需要渐进精确的全局定性解决方案与信息守恒的深度局部定量结果(精度)相结合,反之亦然。为了掌握更可靠的现实表现,并获得更有效的物理和生物模拟技术,研究人员和科学家需要两只智能关节手:随机和组合方法,通过自然耦合协同连接。米兰理工大学(Politecnico di Milano University)自上世纪末以来,首次尝试确定基本原理,以获得更强大的科学应用模拟解决方案。我们新的操作建议是基于CICT内外宇宙(IOU)框架。离散化方法极大地降低了系统表示建模的计算成本和复杂性,显示出了极大的方便性。事实上,CICT表明,N中的任何自然数D都通过OECS(优化指数循环序列)相干信息关联了一个特定的、非任意的EPG-IPG(外部-内部相位发生器)关系,我们必须考虑到这一点,以便在欧几里德空间中计算时充分保留其信息内容。本文是对任意尺度科技系统建模与仿真的相关贡献,展示了CICT如何为更可靠的仿真提供更强大、更有效的系统建模算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The CICT IOU Reference Framework for Stronger AMS System Simulation in Science and Industry
To develop resilient and antifragile application and simulation, we need stronger biological and physical system correlates; in other words, we need asymptotic exact global, qualitative solutions combined to deep local quantitative results (precision) with information conservation and vice-versa. To grasp a more reliable representation of reality and to get more effective physical and biological simulation techniques, researchers and scientists need two intelligently articulated hands: both stochastic and combinatorial approaches synergistically articulated by natural coupling. The first attempt to identify basic principles to get stronger simulation solution for scientific application has been developing at Politecnico di Milano University since the end of last century. Our new operative proposal is based on CICT inner-outer universe (IOU) framework. The discrete approach reveals to be highly convenient because it strongly decreases the computational cost and the complexity of the system for representation modelling. In fact, CICT shows that any natural number D in N has associated a specific, non-arbitrary EPG-IPG (External-Internal Phase Generator) relationship) by OECS (Optimized Exponential Cyclic Sequence) coherence information that we must take into account to full conserve its information content by computation in Euclidean space. This paper is a relevant contribute towards arbitrary-scale scientific and technical systems modeling and simulation, to show how CICT can offer stronger and more effective system modeling algorithms for more reliable simulation.
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