The Effect of Chemical Representation on Active Machine Learning Towards Closed-Loop Optimization

2022 ◽  
Author(s):  
Alexander Pomberger ◽  
Antonio Pedrina McCarthy ◽  
Ahmad Khan ◽  
Simon Sung ◽  
Connor Taylor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chemical Reaction ◽  
Closed Loop ◽  
Reaction Yield ◽  
Catalytic Systems ◽  
Loop Optimization ◽  
Reaction Optimization ◽  
Initial Dataset ◽  
Dataset Size ◽  
The Impact

Multivariate chemical reaction optimization involving catalytic systems is a non-trivial task due to the high number of tuneable parameters and discrete choices. Closed-loop optimization featuring active Machine Learning (ML) represents a powerful strategy for automating reaction optimization. However, the translation of chemical reaction conditions into a machine-readable format comes with the challenge of finding highly informative features which accurately capture the factors for reaction success and allow the model to learn efficiently. Herein, we compare the efficacy of different calculated chemical descriptors for a high throughput generated dataset to determine the impact on a supervised ML model when predicting reaction yield. Then, the effect of featurization and size of the initial dataset within a closed-loop reaction optimization was examined. Finally, the balance between descriptor complexity and dataset size was considered. Ultimately, tailored descriptors did not outperform simple generic representations, however, a larger initial dataset accelerated reaction optimization.

scholarly journals Realization of closed-loop optimization of epitaxial titanium nitride thin-film growth via machine learning

2021 ◽  
Vol 16 ◽  
pp. 100296
Author(s):  
I. Ohkubo ◽  
Z. Hou ◽  
J.N. Lee ◽  
T. Aizawa ◽  
M. Lippmaa ◽  
...  
Keyword(s):  
Thin Film ◽  
Machine Learning ◽  
Titanium Nitride ◽  
Closed Loop ◽  
Film Growth ◽  
Thin Film Growth ◽  
Loop Optimization

scholarly journals Statistical Machine Learning in Model Predictive Control of Nonlinear Processes

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1912
Author(s):  
Zhe Wu ◽  
David Rincon ◽  
Quanquan Gu ◽  
Panagiotis D. Christofides
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Closed Loop ◽  
Series Data ◽  
Generalization Error ◽  
Statistical Machine Learning ◽  
The Impact ◽  
Loop Stability

Recurrent neural networks (RNNs) have been widely used to model nonlinear dynamic systems using time-series data. While the training error of neural networks can be rendered sufficiently small in many cases, there is a lack of a general framework to guide construction and determine the generalization accuracy of RNN models to be used in model predictive control systems. In this work, we employ statistical machine learning theory to develop a methodological framework of generalization error bounds for RNNs. The RNN models are then utilized to predict state evolution in model predictive controllers (MPC), under which closed-loop stability is established in a probabilistic manner. A nonlinear chemical process example is used to investigate the impact of training sample size, RNN depth, width, and input time length on the generalization error, along with the analyses of probabilistic closed-loop stability through the closed-loop simulations under Lyapunov-based MPC.

Application of a Combination of Machine Learning and Reservoir Physics to Increase Production in a Mature Waterflood Field in the San Jorge Basin in Argentina

2021 ◽  
Author(s):  
Javier Eusebio Gomez ◽  
Marcelo Robles ◽  
Cristian Di Giuseppe ◽  
Federico Galliano ◽  
Jeronimo Centineo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ensemble Kalman Filter ◽  
Pareto Front ◽  
Closed Loop ◽  
Optimization Techniques ◽  
Large Field ◽  
Initial Model ◽  
Initial Field ◽  
Loop Optimization ◽  
Novel Technology

Abstract This paper presents the process and results of the application of Data Physics to optimize production of a mature field in the Gulf of San Jorge Basin in Argentina. Data Physics is a novel technology that blends the reservoir physics (black oil) used in traditional numerical simulation with machine learning and advanced optimization techniques. Data Physics was described in detail in a prior paper (Sarma, et al SPE-185507-MS) as a physics-based modeling approach augmented by machine learning. In essence, historical production and injection data are assimilated using an Ensemble Kalman Filter (EnKF) to infer the petrophysical parameters and create a predictive model of the field. This model is then used with Evolutionary Algorithms (EA) to find the pareto front for multiple optimization objectives like production, injection and NPV. Ultimately, the main objective of Data Physics is to enable Closed Loop Optimization. The technology was applied on a small section of a very large field in the Gulf of San Jorge comprised of 134 wells including 83 active producers and 27 active water injectors; up to 12 mandrels per well are used to provide with selective injection, while production is carried out in a comingled manner. Production zonal allocation is calculated using an in-house process based on swabbing tests and recovery factors and is used as input to the Data Physics application, while injection allocation is based on tracer logs performed in each injection well twice a year. This paper describes the modeling and optimization phases as well as the implementation in the field and the results obtained after performing two close loop optimization cycles. The initial model was developed between October and December 2018 and initial field implementation took place between January to March 2019. A second optimization cycle was then executed in January 2020 and results observed for several months.

scholarly journals Closed-loop optimization of fast-charging protocols for batteries with machine learning

Nature ◽  
2020 ◽  
Vol 578 (7795) ◽  
pp. 397-402 ◽  
Author(s):  
Peter M. Attia ◽  
Aditya Grover ◽  
Norman Jin ◽  
Kristen A. Severson ◽  
Todor M. Markov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Closed Loop ◽  
Loop Optimization ◽  
Fast Charging

scholarly journals Mobility Speed Effect and Neural Network Optimization for Deep MIMO Beamforming in mmWave Networks

2020 ◽  
Vol 12 (6) ◽  
pp. 1-14
Author(s):  
Mustafa S. Aljumaily ◽  
Husheng Li
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Network Architecture ◽  
Base Stations ◽  
Neural Network Architecture ◽  
Millimetre Wave ◽  
The Neural Network ◽  
Dataset Size ◽  
Speed Effect ◽  
The Impact

Beamforming for millimetre-wave (mmWave) frequencies has been studied for many years. It is considered as an important enabling technology for communications in these high-frequency ranges and it received a lot of attention in the research community. The special characteristics of the mmWave band made the beamforming problem a challenging one because it depends on many environmental and operational factors. These challenges made any model-based architecture fit only special applications, working scenarios, and specific environment geometry. All these reasons increased the need for more general machine learning based beamforming systems that can work in different environments and conditions. This increased the need for an extended adjustable dataset that can serve as a tool for any machine learning technique to build an efficient beamforming architecture. Deep MIMO dataset has been used in many architectures and designs and has proved its benefits and flexibility to fit in many cases. In this paper, we study the extension of collaborative beamforming that includes many cooperating base stations by studying the impact of User Equipment (UE) speed ranges on the beamforming performance, optimizing the parameters of the neural network architecture of the beamforming design, and suggesting the optimal design that gives the best performance for as a small dataset as possible. Suggested architecture can achieve the same performance achieved before with up to 33% reduction in the dataset size used to train the system which provides a huge reduction in the data collection and processing time.

scholarly journals Impact of Model Architecture Against Adversarial Example's Effectivity

2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Vihan Karnala ◽  
Marianne Campbell
Keyword(s):  
Machine Learning ◽  
Closed Loop ◽  
Machine Learning Algorithms ◽  
Future Research ◽  
Significant Difference ◽  
Adversarial Examples ◽  
End To End ◽  
The Impact ◽  
Behavioral Cloning ◽  
Increased Susceptibility

The purpose of this study is to gain an understanding of the impact of model architecture on the efficacy of adversarial examples against machine learning systems implemented in self-driving applications. Prior research shows how to create and train against adversarial examples in many use cases; however, there is no definite understanding of how a machine learning model’s architecture affects the efficacy of adversarial examples. Data was collected through an experimental setting involving end-to-end self-driving models trained through behavioral cloning. Three model types were tested based on popular frameworks for machine learning algorithms dealing with images. Results showed a statistically significant difference in the impact of adversarial examples between these models. This means that certain model types and architectures are more susceptible to attacks. Therefore, the conclusion can be made that model architecture does impact the efficacy of adversarial examples; however, this is potentially limited to closed-loop, end-to-end systems in which algorithms make the entire decision. Future research should investigate what specific structure within models causes increased susceptibility to adversarial attacks.

The Impact of Visual Feedback Type on the Mastery of Visuo-Motor Transformations

2012 ◽  
Vol 220 (1) ◽  
pp. 3-9 ◽  
Author(s):  
Sandra Sülzenbrück
Keyword(s):  
Empirical Research ◽  
Visual Feedback ◽  
Closed Loop ◽  
Motor Planning ◽  
Visual Input ◽  
Closed Loop Control ◽  
Loop Control ◽  
Feedback Type ◽  
Effective Use ◽  
The Impact

For the effective use of modern tools, the inherent visuo-motor transformation needs to be mastered. The successful adjustment to and learning of these transformations crucially depends on practice conditions, particularly on the type of visual feedback during practice. Here, a review about empirical research exploring the influence of continuous and terminal visual feedback during practice on the mastery of visuo-motor transformations is provided. Two studies investigating the impact of the type of visual feedback on either direction-dependent visuo-motor gains or the complex visuo-motor transformation of a virtual two-sided lever are presented in more detail. The findings of these studies indicate that the continuous availability of visual feedback supports performance when closed-loop control is possible, but impairs performance when visual input is no longer available. Different approaches to explain these performance differences due to the type of visual feedback during practice are considered. For example, these differences could reflect a process of re-optimization of motor planning in a novel environment or represent effects of the specificity of practice. Furthermore, differences in the allocation of attention during movements with terminal and continuous visual feedback could account for the observed differences.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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