Prediction of Chemical-Physical Properties by Neural Networks for Structures

2006 ◽  
Vol 234 (1) ◽  
pp. 13-19 ◽  
Author(s):  
Celia Duce ◽  
Alessio Micheli ◽  
Roberto Solaro ◽  
Antonina Starita ◽  
Maria Rosaria Tiné
Keyword(s):  
Neural Networks ◽  
Physical Properties

Simplifying neural networks for controlling walking by exploiting physical properties

1996 ◽  
pp. 433-438 ◽  
Author(s):  
Holk Cruse ◽  
Christian Bartling ◽  
Jeffrey Dean ◽  
Thomas Kindermann ◽  
Josef Schmitz ◽  
...  
Keyword(s):  
Neural Networks ◽  
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 Predicting permeability via statistical learning on higher-order microstructural information

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Magnus Röding ◽  
Zheng Ma ◽  
Salvatore Torquato
Keyword(s):  
Neural Networks ◽  
Physical Properties ◽  
Specific Surface ◽  
Correlation Functions ◽  
Quantitative Structure ◽  
Structure Property ◽  
Complex Materials ◽  
Structure Property Relationships ◽  
Quantitative Structure Property Relationships ◽  
Point Correlation

Abstract Quantitative structure–property relationships are crucial for the understanding and prediction of the physical properties of complex materials. For fluid flow in porous materials, characterizing the geometry of the pore microstructure facilitates prediction of permeability, a key property that has been extensively studied in material science, geophysics and chemical engineering. In this work, we study the predictability of different structural descriptors via both linear regressions and neural networks. A large data set of 30,000 virtual, porous microstructures of different types, including both granular and continuous solid phases, is created for this end. We compute permeabilities of these structures using the lattice Boltzmann method, and characterize the pore space geometry using one-point correlation functions (porosity, specific surface), two-point surface-surface, surface-void, and void-void correlation functions, as well as the geodesic tortuosity as an implicit descriptor. Then, we study the prediction of the permeability using different combinations of these descriptors. We obtain significant improvements of performance when compared to a Kozeny-Carman regression with only lowest-order descriptors (porosity and specific surface). We find that combining all three two-point correlation functions and tortuosity provides the best prediction of permeability, with the void-void correlation function being the most informative individual descriptor. Moreover, the combination of porosity, specific surface, and geodesic tortuosity provides very good predictive performance. This shows that higher-order correlation functions are extremely useful for forming a general model for predicting physical properties of complex materials. Additionally, our results suggest that artificial neural networks are superior to the more conventional regression methods for establishing quantitative structure–property relationships. We make the data and code used publicly available to facilitate further development of permeability prediction methods.

scholarly journals Simulation of Complex Movements Using Artificial Neural Networks

1998 ◽  
Vol 53 (7-8) ◽  
pp. 628-638 ◽  
Author(s):  
Hoik Cruse ◽  
Jeffrey Dean ◽  
Thomas Kindermann ◽  
Josef Schmitz ◽  
Michael Schumm
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Physical Properties ◽  
Positive Feedback ◽  
Joint Level ◽  
Compliant Substrates ◽  
Non Linear ◽  
Simulated Network ◽  
The Individual ◽  
Recurrent Connections

Abstract A simulated network for controlling a six-legged, insect-like walking system is proposed. The network contains internal recurrent connections, but important recurrent connections utilize the loop through the environment. This approach leads to a subnet for controlling the three joints of a leg during its swing which is arguably the simplest possible solution. The task for the stance subnet appears more difficult because the movements of a larger and varying number of joints (9 -18: three for each leg in stance) have to be controlled such that each leg contributes efficiently to support and propulsion and legs do not work at cross purposes. Already inherently non-linear, this task is further complicated by four factors: 1) the combination of legs in stance varies continuously, 2) during curve walking, legs must move at different speeds, 3) on compliant substrates, the speed of the individual leg may vary unpredictably, and 4) the geometry of the system may vary through growth and injury or due to non-rigid suspension of the joints. This task appears to require some kind of “motor intelligence”. We show that an extremely decentralized, simple controller, based on a combi­nation of negative and positive feedback at the joint level, copes with all these problems by exploiting the physical properties of the system.

scholarly journals ANN analysis in a vision approach for potato inspection

2008 ◽  
Vol 6 (02) ◽  
Author(s):  
R. Rios-Cabrera ◽  
I Lopez-Juarez ◽  
Hsieh Sheng-Jen
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Feature Extraction ◽  
Artificial Neural Networks ◽  
Comparative Analysis ◽  
Physical Properties ◽  
Real Time ◽  
The Other ◽  
Stability And Convergence

An image processing methodology for the extraction of potato properties is explained. The objective is to determine their quality evaluating physical properties and using Artificial Neural Networks (ANN’s) to find misshapen potatoes. A comparative analysis for three connectionist models (Backpropagation, Perceptron and FuzzyARTMAP), evaluating speed and stability for classifying extracted properties is presented. The methodology for image processing and pattern feature extraction is presented together with some results. These results showed that FuzzyARTMAP outperformed the other models due to its stability and convergence speed with times as low as 1 ms per pattern which demonstrates its suitability for real-time inspection. Several algorithms to determine potato defects such as greening, scab, cracks are proposed which can be affectively used for grading different quality of potatoes.

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.

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