Riser Design Automation with Machine Learning

2019 ◽  
Author(s):  
Subrata Bhowmik ◽  
Gautier Noiray ◽  
Harit Naik
Keyword(s):  
Machine Learning ◽  
Design Automation ◽  
Riser Design

scholarly journals Recent Advances on the Design Automation for Performance-Optimized Fiber Reinforced Polymer Composite Components

2020 ◽  
Vol 4 (2) ◽  
pp. 61
Author(s):  
Yi Di Boon ◽  
Sunil Chandrakant Joshi ◽  
Somen Kumar Bhudolia ◽  
Goram Gohel
Keyword(s):  
Machine Learning ◽  
Polymer Composite ◽  
Design Automation ◽  
Material Design ◽  
Fiber Reinforced Polymer ◽  
Fiber Reinforced ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Reinforced Polymer ◽  
Applications Of Machine Learning

Advanced manufacturing techniques, such as automated fiber placement and additive manufacturing enables the fabrication of fiber-reinforced polymer composite components with customized material and structural configurations. In order to take advantage of this customizability, the design process for fiber-reinforced polymer composite components needs to be improved. Machine learning methods have been identified as potential techniques capable of handling the complexity of the design problem. In this review, the applications of machine learning methods in various aspects of structural component design are discussed. They include studies on microstructure-based material design, applications of machine learning models in stress analysis, and topology optimization of fiber-reinforced polymer composites. A design automation framework for performance-optimized fiber-reinforced polymer composite components is also proposed. The proposed framework aims to provide a comprehensive and efficient approach for the design and optimization of fiber-reinforced polymer composite components. The challenges in building the models required for the proposed framework are also discussed briefly.

scholarly journals Machine learning enables design automation of microfluidic flow-focusing droplet generation

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ali Lashkaripour ◽  
Christopher Rodriguez ◽  
Noushin Mehdipour ◽  
Rizki Mardian ◽  
David McIntyre ◽  
...  
Keyword(s):  
Machine Learning ◽  
Design Automation ◽  
Large Scale ◽  
Droplet Diameter ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Droplet Generation ◽  
Data Sets ◽  
Flow Focusing ◽  
Predictive Understanding

AbstractDroplet-based microfluidic devices hold immense potential in becoming inexpensive alternatives to existing screening platforms across life science applications, such as enzyme discovery and early cancer detection. However, the lack of a predictive understanding of droplet generation makes engineering a droplet-based platform an iterative and resource-intensive process. We present a web-based tool, DAFD, that predicts the performance and enables design automation of flow-focusing droplet generators. We capitalize on machine learning algorithms to predict the droplet diameter and rate with a mean absolute error of less than 10 μm and 20 Hz. This tool delivers a user-specified performance within 4.2% and 11.5% of the desired diameter and rate. We demonstrate that DAFD can be extended by the community to support additional fluid combinations, without requiring extensive machine learning knowledge or large-scale data-sets. This tool will reduce the need for microfluidic expertise and design iterations and facilitate adoption of microfluidics in life sciences.

Optimization of amplifier circuits by using gradient boosted trees and probability annealing policy

2020 ◽  
Vol 15 (3) ◽  
pp. 1-5
Author(s):  
Evelyn Cristina de Oliveira Lima ◽  
André Borges Cavalcante ◽  
João Viana Da Fonseca Neto
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Analog Circuits ◽  
Design Automation ◽  
Optimization Problem ◽  
State Of The Art ◽  
Circuit Parameter ◽  
Learning Module ◽  
Circuit Components ◽  
Parameter Values

One important step of the optimization of analog circuits is to properly size circuit components. Since the quantities that define specification may compete for different circuit parameter values, the optimization of analog circuits befits a hard and costly optimization problem. In this work, we propose two contributions to design automation methodologies based on machine learning. Firstly, we propose a probability annealing policy to boost early data collection and restrict electronic simulations later on in the optimization. Secondly, we employ multiple gradient boosted trees to predict design superiority, which reduces overfitting to learned designs. When compared to the state-of-the art, our approach reduces the number of electronic simulations, the number of queries made to the machine learning module required to finish the optimization.

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