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machine learningbased modeling with optimization

machine learningbased modeling with optimization

Machine Learning-Based Modeling with Optimization ...

2021-3-4  Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete Muhammad Izhar Shah,1 Shazim Ali

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(PDF) Machine Learning-Based Modeling with

Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete March 2021 Advances in Civil Engineering 2021(9):1-15

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Machine Learning-Based Modeling with Optimization ...

Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete Muhammad Izhar Shah, Shazim Ali Memon , Muhammad Sohaib Khan Niazi, Muhammad Nasir Amin, Fahid Aslam, Muhammad Faisal Javed

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"Machine Learning-based Modeling and Optimization of ...

The resulting dataset was crucial in creating the machine learning-based model that is the core of this work. The model developed uses two random forests in a unique, multi-layer deep learning-type structure to accommodate the combination of independent and

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A machine learning-based surrogate model for

An equivalent optimization problem is constructed based on the original objective function and the DNN model. (iv) The DE algorithm is introduced to resolve the optimization problem. Download : Download high-res image (424KB) Download : Download full-size image; Fig. 4. Flowchart depicting the deep neural network-based surrogate model for ...

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Machine learning-based surrogate modeling for data

2019-5-9  @article{osti_1642435, title = {Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques}, author = {Kim, Sun Hye and Boukouvala, Fani}, abstractNote = {Optimization of simulation-based or data-driven systems is a challenging task, which has attracted significant attention in the recent literature.

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Machine Learning-Based Reverse Modeling Approach for

2020-2-19  This paper focuses on efficient computational optimization algorithms for the generation of micro electro discharge machining (µEDM) tool shapes. In a previous paper, the authors presented a reliable reverse modeling approach to perform such tasks based on a crater-by-crater simulation model and an outer optimization loop.

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Machine learning-based optimization of process

2021-4-16  In order to improve the Ti–6Al–4V SLM-fabricated part quality and help the manufacturing engineers choose optimal process parameters, an optimization methodology based on an artificial neural network was developed to relate four key process parameters (laser power, laser scanning speed, layer thickness, and hatch distance) and two target properties of a part fabricated by the SLM technique

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Machine learning-based surrogate modeling for data

2019-5-9  Optimization of simulation-based or data-driven systems is a challenging task, which has attracted significant attention in the recent literature. A very efficient approach for optimizing systems without analytical expressions is through fitting surrogate models. Due to their increased flexibility, nonlinear interpolating functions, such as radial basis functions and Kriging, have been ...

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A machine learning-based multiscale model to predict

2021-8-20  The study develops a machine learning approach for predicting bone regeneration in an additively manufactured bioceramic scaffold, which is correlated with an in vivo sheep model, exhibiting ...

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Machine Learning-based Modeling and Optimization of ...

2018-11-28  The resulting dataset was crucial in creating the machine learning-based model that is the core of this work. The model developed uses two random forests in a unique, multi-layer deep learning-type structure to accommodate the combination of independent and partially-dependent variables that describe this system.

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Multi-scale and machine learning-based modeling

2021-7-29  The mini-symposium "Multi-scale and machine learning-based modeling methods for optimization and design of composites" aims at outlining the state-of-the-art and the perspectives of the research in the field of simulations of advanced composites materials and structures.

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An Overview of Machine Learning-Based Techniques for ...

2021-5-12  For instance, machine learning and its offsprings are trending because of their enhanced capabilities in automating analytical modeling. In this realm, learning-based techniques (supervised, unsupervised, and reinforcement) have grown to complement many of the optimization problems in testing and training.

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On Wireless Systems Modelling and Machine Learning

2017-4-5  Machine learning based inter-cell interference coordination and offloading solutions will be presented together with results obtained from such a system level simulation environment. In the second part, emerging future wireless systems, their key features, and

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Machine Learning-Based Rate Distortion Modeling for

2021-6-9  Rate-distortion (R-D) optimization has been widely adopted to improve the coding efficiency on the video encoder side. However, there are few studies related to modeling the R-D characteristics of the latest Versatile Video Coding (VVC) reference software. In this paper, we investigate the R-D modeling of the intra-frame on the VVC encoder by utilizing four traditional machine learning ...

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The Hitchhiker’s Guide to Optimization in Machine

2021-6-5  Machine Learning is the ideal culmination of Applied Mathematics and Computer Science, where we train and use data-driven applications to run inferences on the available data. In this article, you get to learn what optimizing an ML model means, with an overview of Gradient Descent and Stochastic Gradient Descent (SGD).

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Machine learning-based surrogate modeling for data

2019-5-9  Optimization of simulation-based or data-driven systems is a challenging task, which has attracted significant attention in the recent literature. A very efficient approach for optimizing systems without analytical expressions is through fitting surrogate models. Due to their increased flexibility, nonlinear interpolating functions, such as radial basis functions and Kriging, have been ...

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Modeling, design, and machine learning-based framework

2021-8-3  Numerical models coupled with statistical approaches have been widely implemented in design optimization of various engineering systems (24–26) and, more recently, used for biomedical applications (27–34). The used modeling approach of the clog formation criterion provided a novel method to address the barrier in current numerical software ...

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(PDF) A Novel Machine Learning-Based Analysis Model for ...

2021-8-9  Some models that use machine learning to detect smart contract vulnerabilities cost much time in extracting features manually. In this paper, we introduce a novel machine learning-based analysis ...

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MACHINE LEARNINGBASED GENERIC LOAD

2014-10-27  MACHINE LEARNINGBASED GENERIC LOAD FORECASTINGMODEL FOR NOISY ... model formulation is presented, which incorporates machine learning techniques for data pre-processing, analysis, and model development. Key ... model development, optimization, and data preprocessing. An analytical study is performed on

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Machine Learning-Based Modeling with Optimization ...

Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete Table 5 Results of laboratory-derived mechanical properties of SCBA concrete.

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Modeling and Optimization for Machine Learning ...

2021-6-14  Optimization algorithms lie at the heart of machine learning (ML) and artificial intelligence (AI). The distinctive feature of optimization within ML is the strong departure from textbook approaches: the focus is now on a different set of goals driven by big

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Modeling Pipeline Optimization With scikit-learn

2021-6-19  Modeling Pipeline Optimization With scikit-learn. This tutorial presents two essential concepts in data science and automated learning. One is the machine learning pipeline, and the second is its optimization. These two principles are the key to implementing any successful intelligent system based on machine learning.

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The Hitchhiker’s Guide to Optimization in Machine

2021-6-5  Machine Learning is the ideal culmination of Applied Mathematics and Computer Science, where we train and use data-driven applications to run inferences on the available data. In this article, you get to learn what optimizing an ML model means, with an overview of Gradient Descent and Stochastic Gradient Descent (SGD).

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A novel machine learning-based optimization algorithm ...

2021-1-15  DOE PAGES Journal Article: A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design. This content will become publicly available on Sat Jan 15 00:00:00 EST 2022 ...

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Tuning and Optimizing Machine Learning Models -

2021-8-14  Through optimization, the hyperparameters are adjusted after each training run for the best outcome based on the scoring metric. This iterative process is also referred to as model tuning. Each aspect of machine learning is continuously evolving, with new optimization techniques around different applications and models.

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Learning-Based Modeling and Optimization for Real

2020-5-1  Learning-Based Modeling and Optimization for Real-time System Availability Liying Li, Junlong Zhou, Mingsong Chen, Tongquan Wei, and Xiaobo Sharon Hu Abstract—As the density of integrated circuits continues to increase, the possibility that real-time systems suffer from soft and hard

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(PDF) A Novel Machine Learning-Based Analysis Model for ...

2021-8-9  Some models that use machine learning to detect smart contract vulnerabilities cost much time in extracting features manually. In this paper, we introduce a novel machine learning-based analysis ...

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MACHINE LEARNINGBASED GENERIC LOAD

2014-10-27  MACHINE LEARNINGBASED GENERIC LOAD FORECASTINGMODEL FOR NOISY ... model formulation is presented, which incorporates machine learning techniques for data pre-processing, analysis, and model development. Key ... model development, optimization, and data preprocessing. An analytical study is performed on

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A Fast Machine Learning-based Mask Printability

2020-4-6  modeling; Yield and cost optimization; KEYWORDS Design for Manufacturability, Optical Proximity Correction Accel-eration, Machine Learning ACM Reference Format: Bentian Jiang, Hang Zhang, Jinglei Yang, and Evangeline F. Y. Young. 2019. A Fast Machine Learning-based Mask Printability Predictor for OPC Ac-celeration.

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