scholarly journals Machine learning dynamic correlation in chemical kinetics

2021 ◽  
Vol 155 (14) ◽  
pp. 144107
Author(s):  
Changhae Andrew Kim ◽  
Nathan D. Ricke ◽  
Troy Van Voorhis
Keyword(s):  
Machine Learning ◽  
Chemical Kinetics ◽  
Dynamic Correlation ◽  
Learning Dynamic

scholarly journals Machine Learning Dynamic Correlation in Chemical Kinetics

2021 ◽  
Author(s):  
Changhae Andrew Kim ◽  
Nathan D. Ricke ◽  
Troy Van Voorhis
Keyword(s):  
Machine Learning ◽  
Chemical Kinetics ◽  
Dynamic Correlation ◽  
Learning Dynamic

scholarly journals Machine Learning Dynamic Correlation in Chemical Kinetics

2021 ◽  
Author(s):  
Changhae Andrew Kim ◽  
Nathan D. Ricke ◽  
Troy Van Voorhis
Keyword(s):  
Machine Learning ◽  
Chemical Kinetics ◽  
Dynamic Correlation ◽  
Learning Dynamic

scholarly journals Learning dynamic Boltzmann distributions as reduced models of spatial chemical kinetics

2018 ◽  
Vol 149 (3) ◽  
pp. 034107 ◽  
Author(s):  
Oliver K. Ernst ◽  
Thomas Bartol ◽  
Terrence Sejnowski ◽  
Eric Mjolsness
Keyword(s):  
Chemical Kinetics ◽  
Reduced Models ◽  
Learning Dynamic

Toward Compact, Numerically Stable Machine Learning Functions for Chemical Kinetics

2021 ◽  
Author(s):  
Alisha J. Sharma ◽  
Ryan F. Johnson ◽  
Adam Moses ◽  
David A. Kessler
Keyword(s):  
Machine Learning ◽  
Chemical Kinetics ◽  
Learning Functions

A New Method of Machine Learning Based on Examples

2011 ◽  
Vol 204-210 ◽  
pp. 1078-1081
Author(s):  
De Gan Zhang ◽  
Dong Wang ◽  
Yu Xia Hu ◽  
Xue Jing Kang
Keyword(s):  
Machine Learning ◽  
Classical Method ◽  
Training Data ◽  
New Method ◽  
Information Mining ◽  
Dynamic Correlation

A new method of machine learning based on examples is given in this paper. This method improves the classical method ID3 which learns from static examples. Its limits lie on no comprehension and no memory, and no dynamic correlation. In the new method, it can learn from dynamic examples, the change of data can be learned because the training data is the initial and end process in the interval. All varieties and correlation can be understood and remembered. By experiments, the method can be used as classifier and it has special use in the field of information mining.

scholarly journals Machine Learning Based Dynamic Correlation on Marine Environmental Data Using Cross-Recurrence Strategy

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 185121-185130
Author(s):  
Zhigang Li ◽  
Di Cai ◽  
Jialin Wang ◽  
Yingqi Li ◽  
Guan Gui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Environmental Data ◽  
Dynamic Correlation ◽  
Marine Environmental

scholarly journals Reverse engineering and identification in systems biology: strategies, perspectives and challenges

2014 ◽  
Vol 11 (91) ◽  
pp. 20130505 ◽  
Author(s):  
Alejandro F. Villaverde ◽  
Julio R. Banga
Keyword(s):  
Machine Learning ◽  
Inverse Problem ◽  
Systems Biology ◽  
Mathematical Modelling ◽  
Reverse Engineering ◽  
Chemical Kinetics ◽  
Regulatory Mechanisms ◽  
Engineering Problems ◽  
Central Elements ◽  
Key Questions

The interplay of mathematical modelling with experiments is one of the central elements in systems biology. The aim of reverse engineering is to infer, analyse and understand, through this interplay, the functional and regulatory mechanisms of biological systems. Reverse engineering is not exclusive of systems biology and has been studied in different areas, such as inverse problem theory, machine learning, nonlinear physics, (bio)chemical kinetics, control theory and optimization, among others. However, it seems that many of these areas have been relatively closed to outsiders. In this contribution, we aim to compare and highlight the different perspectives and contributions from these fields, with emphasis on two key questions: (i) why are reverse engineering problems so hard to solve, and (ii) what methods are available for the particular problems arising from systems biology?

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