scholarly journals Machine learning–enabled identification of material phase transitions based on experimental data: Exploring collective dynamics in ferroelectric relaxors

2018 ◽  
Vol 4 (3) ◽  
pp. eaap8672 ◽  
Author(s):  
Linglong Li ◽  
Yaodong Yang ◽  
Dawei Zhang ◽  
Zuo-Guang Ye ◽  
Stephen Jesse ◽  
...  
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Phase Transitions ◽  
Collective Dynamics ◽  
Ferroelectric Relaxors

scholarly journals Unsupervised machine learning of topological phase transitions from experimental data

2021 ◽  
Author(s):  
Niklas Käming ◽  
Anna Dawid ◽  
Korbinian Kottmann ◽  
Maciej Lewenstein ◽  
Klaus Sengstock ◽  
...  
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Phase Transitions ◽  
Topological Phase ◽  
Unsupervised Machine Learning ◽  
Topological Phase Transitions

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

scholarly journals Smart Design Nano-Hybrid Formulations by Machine Learning

Proceedings ◽  
2020 ◽  
Vol 78 (1) ◽  
pp. 5
Author(s):  
Raquel de Melo Barbosa ◽  
Fabio Fonseca de Oliveira ◽  
Gabriel Bezerra Motta Câmara ◽  
Tulio Flavio Accioly de Lima e Moura ◽  
Fernanda Nervo Raffin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Water Solubility ◽  
Inorganic Materials ◽  
Fine Tuning ◽  
Support Vector ◽  
Drug Solubility ◽  
Physical Behavior ◽  
Best Fit

Nano-hybrid formulations combine organic and inorganic materials in self-assembled platforms for drug delivery. Laponite is a synthetic clay, biocompatible, and a guest of compounds. Poloxamines are amphiphilic four-armed compounds and have pH-sensitive and thermosensitive properties. The association of Laponite and Poloxamine can be used to improve attachment to drugs and to increase the solubility of β-Lapachone (β-Lap). β-Lap has antiviral, antiparasitic, antitumor, and anti-inflammatory properties. However, the low water solubility of β-Lap limits its clinical and medical applications. All samples were prepared by mixing Tetronic 1304 and LAP in a range of 1–20% (w/w) and 0–3% (w/w), respectively. The β-Lap solubility was analyzed by UV-vis spectrophotometry, and physical behavior was evaluated across a range of temperatures. The analysis of data consisted of response surface methodology (RMS), and two kinds of machine learning (ML): multilayer perceptron (MLP) and support vector machine (SVM). The ML techniques, generated from a training process based on experimental data, obtained the best correlation coefficient adjustment for drug solubility and adequate physical classifications of the systems. The SVM method presented the best fit results of β-Lap solubilization. In silico tools promoted fine-tuning, and near-experimental data show β-Lap solubility and classification of physical behavior to be an excellent strategy for use in developing new nano-hybrid platforms.

ON AN ORIENTATION TRANSITION IN NEMATIC LIQUID CRYSTALS INDUCED BY THE THICKNESS OF THE SAMPLE

1993 ◽  
Vol 07 (18) ◽  
pp. 1215-1222
Author(s):  
A. L. ALEXE-IONESCU
Keyword(s):  
Experimental Data ◽  
Phase Transitions ◽  
Liquid Crystal ◽  
Nematic Liquid Crystal ◽  
Tilt Angle ◽  
Critical Thickness ◽  
Large Range ◽  
Square Root ◽  
Chiral Molecules ◽  
Homeotropic Orientation

An orientation transition observed in nematic liquid crystal samples, induced by the thickness, is interpreted in a new way. By supposing that the nematic liquid crystal contains chiral impurities, it is shown that the homeotropic orientation is stable only for thicknesses smaller than a critical one, and is dependent on the concentration of the chiral molecules. At the critical thickness, the transition from the homeotropic orientation to the distorted one is characterized by a tilt angle proportional to the square root of the actual thickness minus the critical one. This trend is typical of second order phase transitions. The agreement between the theory and the experimental data is fairly good over a large range of thickness of the sample.

Risk-Controlled Wellbore Stability Criterion Based on Machine Learning Assisted Finite Element Model

2021 ◽  
Author(s):  
Hussain AlBahrani ◽  
Nobuo Morita
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Finite Element ◽  
Finite Element Model ◽  
Stability Criterion ◽  
Element Model ◽  
Rock Failure ◽  
Wellbore Stability ◽  
Wellbore Instability ◽  
Conventional Methods

Abstract In many drilling scenarios that include deep wells and highly stressed environments, the mud weight required to completely prevent wellbore instability can be impractically high. In such cases, what is known as risk-controlled wellbore stability criterion is introduced. This criterion allows for a certain level of wellbore instability to take place. This means that the mud weight calculated using this criterion will only constrain wellbore instability to a certain manageable level, hence the name risk-controlled. Conventionally, the allowable level of wellbore instability in this type of models has always been based on the magnitude of the breakout angle. However, wellbore enlargements, as seen in calipers and image logs, can be highly irregular in terms of its distribution around the wellbore. This irregularity means that risk-controlling the wellbore instability through the breakout angle might not be always sufficient. Instead, the total volume of cavings is introduced as the risk control parameter for wellbore instability. Unlike the breakout angle, the total volume of cavings can be coupled with a suitable hydraulics model to determine the threshold of manageable instability. The expected total volume of cavings is determined using a machine learning (ML) assisted 3D elasto-plastic finite element model (FEM). The FEM works to model the interval of interest, which eventually provides a description of the stress distribution around the wellbore. The ML algorithm works to learn the patterns and limits of rock failure in a supervised training manner based on the wellbore enlargement seen in calipers and image logs from nearby offset wells. Combing the FEM output with the ML algorithm leads to an accurate prediction of shear failure zones. The model is able to predict both the radial and circumferential distribution of enlargements at any mud weight and stress regime, which leads to a determination of the expected total volume of cavings. The model implementation is first validated through experimental data. The experimental data is based on true-triaxial tests of bored core samples. Next, a full dataset from offset wells is used to populate and train the model. The trained model is then used to produce estimations of risk-controlled stability mud weights for different drilling scenarios. The model results are compared against those produced by conventional methods. Finally, both the FEM-ML model and the conventional methods results are compared against the drilling experience of the offset wells. This methodology provides a more comprehensive and new solution to risk controlling wellbore instability. It relies on a novel process which learns rock failure from calipers and image logs.

Software-in-the-Loop Combined Machine Learning for Dynamic Responses Analysis of Floating Offshore Wind Turbines

2021 ◽  
Author(s):  
Peng Chen ◽  
Changhong Hu ◽  
Zhiqiang Hu
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Wind Turbines ◽  
Dynamic Responses ◽  
Offshore Wind ◽  
Learning Technology ◽  
Mean Values ◽  
Offshore Wind Turbines ◽  
Floating Offshore Wind Turbines ◽  
Brute Force Method

Abstract Artificial intelligence (AI) brings a new solution to overcome the challenges of Floating offshore wind turbines (FOWTs) to better predict the dynamic responses with intelligent strategies. A new AI-based software-in-the-loop method, named SADA is introduced in this paper for the prediction of dynamic responses of FOWTs, which is proposed based on an in-house programme DARwind. DARwind is a coupled aero-hydro-servo-elastic in-house program for FOWTs, and a reinforcement learning method with exhaust algorithm and deep deterministic policy gradient (DDPG) are embedded in DARwind as an AI module. Firstly, the methodology is introduced with the selection of Key Disciplinary Parameters (KDPs). Secondly, Brute-force Method and DDPG algorithms are adopted to changes the KDPs’ values according to the feedback of 6DOF motions of Hywind Spar-type platform through comparing the DARwind simulation results and those of basin experimental data. Therefore, many other dynamic responses that cannot be measured in basin experiment can be predicted in good accuracy with SADA method. Finally, the case study of SADA method was conducted and the results demonstrated that the mean values of the platform’s motions can be predicted with higher accuracy. This proposed SADA method takes advantage of numerical-experimental method, basin experimental data and the machine learning technology, which brings a new and promising solution for overcoming the handicap impeding direct use of conventional basin experimental way to analyze FOWT’s dynamic responses during the design phase.

scholarly journals Unsupervised machine learning for detection of phase transitions in off-lattice systems. II. Applications

2018 ◽  
Vol 149 (19) ◽  
pp. 194110 ◽  
Author(s):  
R. B. Jadrich ◽  
B. A. Lindquist ◽  
W. D. Piñeros ◽  
D. Banerjee ◽  
T. M. Truskett
Keyword(s):  
Machine Learning ◽  
Phase Transitions ◽  
Lattice Systems ◽  
Unsupervised Machine Learning

scholarly journals Unsupervised machine learning for detection of phase transitions in off-lattice systems. I. Foundations

2018 ◽  
Vol 149 (19) ◽  
pp. 194109 ◽  
Author(s):  
R. B. Jadrich ◽  
B. A. Lindquist ◽  
T. M. Truskett
Keyword(s):  
Machine Learning ◽  
Phase Transitions ◽  
Lattice Systems ◽  
Unsupervised Machine Learning

scholarly journals Adaptive Intelligent Welding Manufacturing

2021 ◽  
Vol 100 (01) ◽  
pp. 63-83
Author(s):  
YUMING ZHANG ◽  
◽  
QIYUE WANG ◽  
YUKANG LIU
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Deep Learning ◽  
Welding Process ◽  
Welding Parameters ◽  
Weld Quality ◽  
Robotic Welding ◽  
Process Variables ◽  
Physical Constraints ◽  
Needed Information

Optimal design of the welding procedure gives the desired welding results under nominal welding conditions. During manufacturing, where the actual welding manufacturing conditions often deviate from the nominal ones used in the design, applying the designed procedure will produce welding results that are different from the desired ones. Adaption is needed to make corrections and adjust some of the welding parameters from those specified in the design. This is adaptive welding. While human welders can be adaptive to make corrections and adjustments, their performance is limited by their physical constraints and skill level. To be adaptive, automated and robotic welding systems require abilities in sensing the welding process, extracting the needed information from signals from the sensors, predicting the responses of the welding process to the adjustments on welding parameters, and optimizing the adjustments. This results in the application of classical sensing, modeling of process dynamics, and control system design. In many cases, the needed information for the weld quality and process variables of our concern is not easy to extract from the sensor’s data. Studies are needed to propose the phenomena to sense and establish the scientific foundation to correlate them to the weld quality or process variables of our concern. Such studies can be labor intensive, and a more automated approach is needed. Analysis suggests that artificial intelligence and machine learning, especially deep learning, can help automate the learning such that the needed intelligence for robotic welding adaptation can be directly and automatically learned from experimental data after the physical phenomena being represented by the experimental data has been appropriately selected to make sure they are fundamentally correlated to that with which we are concerned. Some adaptation abilities may also be learned from skilled human welders. In addition, human-robot collaborative welding may incorporate adaptations from humans with the welding robots. This paper analyzes and identifies the challenges in adaptive robotic welding, reviews efforts devoted to solve these challenges, analyzes the principles and nature of the methods behind these efforts, and introduces modern approaches, including machine learning/deep learning, learning from humans, and human-robot collaboration, to solve these challenges.

Phase Transitions in Machine Learning

2016 ◽  
pp. 1-8
Author(s):  
Lorenza Saitta ◽  
Michele Sebag
Keyword(s):  
Machine Learning ◽  
Phase Transitions
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