scholarly journals Inference of Hidden Structures in Complex Physical Systems by Multi-scale Clustering

2015 ◽  
pp. 115-138 ◽  
Author(s):  
Z. Nussinov ◽  
P. Ronhovde ◽  
Dandan Hu ◽  
S. Chakrabarty ◽  
Bo Sun ◽  
...  
Keyword(s):  
Physical Systems ◽  
Multi Scale

Multi-scale approach from mechatronic to Cyber-Physical Systems for the design of manufacturing systems

2017 ◽  
Vol 86 ◽  
pp. 52-69 ◽  
Author(s):  
Olivia Penas ◽  
Régis Plateaux ◽  
Stanislao Patalano ◽  
Moncef Hammadi
Keyword(s):  
Manufacturing Systems ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Multi Scale

scholarly journals Deep Learning for Variational Multi-Scale Molecular Modeling

2020 ◽  
Author(s):  
Jun Zhang ◽  
Yaokun Lei ◽  
Yi Isaac Yang ◽  
Yi Qin Gao
Keyword(s):  
Coarse Grained ◽  
Atomistic Simulations ◽  
Molecular Models ◽  
Enhanced Sampling ◽  
Physical Systems ◽  
Molecular Systems ◽  
Fine Grained ◽  
Multi Scale ◽  
Machine Learning Approach ◽  
Training Objective

Molecular simulations are widely applied in the study of chemical and bio-physical systems. However, the<br>accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems<br>containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either<br>sticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveraging<br>coarse-grained models. Although both strategies are promising, either of them, if adopted individually,<br>exhibits severe limitations. In this paper we propose a machine-learning approach to ally both strategies so<br>that simulations on different scales can benefit mutually from their cross-talks: Accurate coarse-grained (CG)<br>models can be inferred from the fine-grained (FG) simulations through deep generative learning; In turn, FG<br>simulations can be boosted by the guidance of CG models via deep reinforcement learning. Our method<br>defines a variational and adaptive training objective which allows end-to-end training of parametric<br>molecular models using deep neural networks. Through multiple experiments, we show that our method is<br>efficient and flexible, and performs well on challenging chemical and bio-molecular systems. <br>

scholarly journals Deep Learning for Variational Multi-Scale Molecular Modeling

2020 ◽  
Author(s):  
Jun Zhang ◽  
Yaokun Lei ◽  
Yi Isaac Yang ◽  
Yi Qin Gao
Keyword(s):  
Coarse Grained ◽  
Atomistic Simulations ◽  
Molecular Models ◽  
Enhanced Sampling ◽  
Physical Systems ◽  
Molecular Systems ◽  
Fine Grained ◽  
Multi Scale ◽  
Machine Learning Approach ◽  
Training Objective

Molecular simulations are widely applied in the study of chemical and bio-physical systems. However, the<br>accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems<br>containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either<br>sticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveraging<br>coarse-grained models. Although both strategies are promising, either of them, if adopted individually,<br>exhibits severe limitations. In this paper we propose a machine-learning approach to ally both strategies so<br>that simulations on different scales can benefit mutually from their cross-talks: Accurate coarse-grained (CG)<br>models can be inferred from the fine-grained (FG) simulations through deep generative learning; In turn, FG<br>simulations can be boosted by the guidance of CG models via deep reinforcement learning. Our method<br>defines a variational and adaptive training objective which allows end-to-end training of parametric<br>molecular models using deep neural networks. Through multiple experiments, we show that our method is<br>efficient and flexible, and performs well on challenging chemical and bio-molecular systems. <br>

scholarly journals Deep Learning for Multi-Scale Molecular Modeling

2020 ◽  
Author(s):  
Jun Zhang ◽  
Yaokun Lei ◽  
Yi Isaac Yang ◽  
Yi Qin Gao
Keyword(s):  
Coarse Grained ◽  
Atomistic Simulations ◽  
Molecular Models ◽  
Enhanced Sampling ◽  
Physical Systems ◽  
Molecular Systems ◽  
Fine Grained ◽  
Multi Scale ◽  
Machine Learning Approach ◽  
Training Objective

Molecular simulations are widely applied in the study of chemical and bio-physical systems. However, the<br>accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems<br>containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either<br>sticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveraging<br>coarse-grained models. Although both strategies are promising, either of them, if adopted individually,<br>exhibits severe limitations. In this paper we propose a machine-learning approach to ally both strategies so<br>that simulations on different scales can benefit mutually from their cross-talks: Accurate coarse-grained (CG)<br>models can be inferred from the fine-grained (FG) simulations through deep generative learning; In turn, FG<br>simulations can be boosted by the guidance of CG models via deep reinforcement learning. Our method<br>defines a variational and adaptive training objective which allows end-to-end training of parametric<br>molecular models using deep neural networks. Through multiple experiments, we show that our method is<br>efficient and flexible, and performs well on challenging chemical and bio-molecular systems. <br>

Emerging Cyber-Physical Systems : An Overview

2018 ◽  
pp. 306-316
Author(s):  
Okolie S.O. ◽  
Kuyoro S.O. ◽  
Ohwo O. B
Keyword(s):  
Health Care ◽  
Economic Benefit ◽  
Physical World ◽  
Cyber Physical Systems ◽  
Cyber Physical System ◽  
Economic Sectors ◽  
Strategic Research ◽  
Physical Systems ◽  
Security Issues ◽  
Wide Range

Cyber-Physical Systems (CPS) will revolutionize how humans relate with the physical world around us. Many grand challenges await the economically vital domains of transportation, health-care, manufacturing, agriculture, energy, defence, aerospace and buildings. Exploration of these potentialities around space and time would create applications which would affect societal and economic benefit. This paper looks into the concept of emerging Cyber-Physical system, applications and security issues in sustaining development in various economic sectors; outlining a set of strategic Research and Development opportunities that should be accosted, so as to allow upgraded CPS to attain their potential and provide a wide range of societal advantages in the future.

A Proposal of 3D Feature based on Occupancy of Point Cloud in Multi-Scale Shell Region

2016 ◽  
Vol 136 (8) ◽  
pp. 1078-1084
Author(s):  
Shoichi Takei ◽  
Shuichi Akizuki ◽  
Manabu Hashimoto
Keyword(s):  
Point Cloud ◽  
Multi Scale ◽  
Feature Based ◽  
Shell Region
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