scholarly journals Artificial Neural Networks for the Prediction of Thermo Physical Properties of Liquid Mixtures

2008 ◽  
Vol 1 (3) ◽  
Author(s):  
R. Baskaran ◽  
S. Arunachalam ◽  
K. Manjunath ◽  
T.R Kubendran
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Physical Properties ◽  
Liquid Mixtures ◽  
Artificial Neural ◽  
Thermo Physical Properties

scholarly journals Constraints of Biological Neural Networks and Their Consideration in AI Applications

2010 ◽  
Vol 2010 ◽  
pp. 1-6 ◽  
Author(s):  
Richard Stafford
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Physical Properties ◽  
Ecological Niche ◽  
Neural Systems ◽  
Ecological Constraints ◽  
Process Information ◽  
Spiking Networks ◽  
Artificial Neural ◽  
Biological Organisms

Biological organisms do not evolve to perfection, but to out compete others in their ecological niche, and therefore survive and reproduce. This paper reviews the constraints imposed on imperfect organisms, particularly on their neural systems and ability to capture and process information accurately. By understanding biological constraints of the physical properties of neurons, simpler and more efficient artificial neural networks can be made (e.g., spiking networks will transmit less information than graded potential networks, spikes only occur in nature due to limitations of carrying electrical charges over large distances). Furthermore, understanding the behavioural and ecological constraints on animals allows an understanding of the limitations of bio-inspired solutions, but also an understanding of why bio-inspired solutions may fail and how to correct these failures.

scholarly journals Exploring Correlations Between Properties Using Artificial Neural Networks

2019 ◽  
Vol 51 (1) ◽  
pp. 58-75
Author(s):  
Yiming Zhang ◽  
Julian R. G. Evans ◽  
Shoufeng Yang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Physical Properties ◽  
Materials Science ◽  
Causal Relationships ◽  
Structure And Properties ◽  
Compositional Field ◽  
Artificial Neural ◽  
Design Materials

Abstract The traditional aim of materials science is to establish the causal relationships between composition, processing, structure, and properties with the intention that, eventually, these relationships will make it possible to design materials to meet specifications. This paper explores another approach. If properties are related to structure at different scales, there may be relationships between properties that can be discerned and used to make predictions so that knowledge of some properties in a compositional field can be used to predict others. We use the physical properties of the elements as a dataset because it is expected to be both extensive and reliable and we explore this method by showing how it can be applied to predict the polarizability of the elements from other properties.

Sign in / Sign up

Export Citation Format

Share Document