DataConsistent Prime模型的瞬时建模和逆向工程只需几秒钟！
Instantaneous Modelling and Reverse Engineering of DataConsistent Prime Models in Seconds!
摘要：
已经发明和开发了一种支持完全从实验时间序列数据自动构建动态素数模型的理论框架，其可以在几秒钟内自动生成（构建）任何时间序列数据的数据驱动模型。这导致了新的逆向工程和动态方法的制定和形式化，用于复杂系统的自动化系统建模，包括复杂的生物，金融，控制和人工神经网络系统。本发明背后的系统/模型理论已经被形式化为与基于过程的建模互补的新的、有效的和鲁棒的系统识别策略。提出的动态建模和网络推理解决方案通常涉及解决极其困难的参数估计挑战，推断未知的底层网络结构，以及复杂系统的智能和智能ODE模型的无监督制定和构建。在欠定条件下，即，在如何从小规模的时间序列数据中即时、快速地构造未知（或已充分研究的）复杂系统的数据一致的基本模型的情况下，对未知的底层相互作用网络的推断更具挑战性。本文报告了对整个主要模型构建过程的一步一步的强大数学和计算分析，该过程在不到一分钟的时间内从数据中确定模型。
英文摘要：
A theoretical framework that supports automated construction of dynamic prime models purely from experimental time series data has been invented and developed, which can automatically generate (construct) data-driven models of any time series data in seconds. This has resulted in the formulation and formalisation of new reverse engineering and dynamic methods for automated systems modelling of complex systems, including complex biological, financial, control, and artificial neural network systems. The systems/model theory behind the invention has been formalised as a new, effective and robust system identification strategy complementary to process-based modelling. The proposed dynamic modelling and network inference solutions often involve tackling extremely difficult parameter estimation challenges, inferring unknown underlying network structures, and unsupervised formulation and construction of smart and intelligent ODE models of complex systems. In underdetermined conditions, i.e., cases of dealing with how best to instantaneously and rapidly construct data-consistent prime models of unknown (or well-studied) complex system from small-sized time series data, inference of unknown underlying network of interaction is more challenging. This article reports a robust step-by-step mathematical and computational analysis of the entire prime model construction process that determines a model from data in less than a minute.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
