Machine Learning Based Physics Inference from High-Fidelity Solutions: Vortex Core Data Extraction

2022 ◽  
Author(s):  
Jennifer Abras ◽  
Nathan S. Hariharan
Keyword(s):  
Machine Learning ◽  
Vortex Core ◽  
Data Extraction ◽  
High Fidelity ◽  
Core Data

Marketing customer response scoring model based on machine learning data analysis

2020 ◽  
pp. 1-11
Author(s):  
Tang Yan ◽  
Li Pengfei
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Data Extraction ◽  
Machine Learning Algorithms ◽  
Customer Relationship ◽  
Customer Data ◽  
Modeling And Analysis ◽  
Scoring Model ◽  
Model Based ◽  
Learning Data

In marketing, problems such as the increase in customer data, the increase in the difficulty of data extraction and access, the lack of reliability and accuracy of data analysis, the slow efficiency of data processing, and the inability to effectively transform massive amounts of data into valuable information have become increasingly prominent. In order to study the effect of customer response, based on machine learning algorithms, this paper constructs a marketing customer response scoring model based on machine learning data analysis. In the context of supplier customer relationship management, this article analyzes the supplier’s precision marketing status and existing problems and uses its own development and management characteristics to improve marketing strategies. Moreover, this article uses a combination of database and statistical modeling and analysis to try to establish a customer response scoring model suitable for supplier precision marketing. In addition, this article conducts research and analysis with examples. From the research results, it can be seen that the performance of the model constructed in this article is good.

Leveraging machine learning technology to efficiently identify and match patients for precision oncology clinical trials.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e13588-e13588
Author(s):  
Laura Sachse ◽  
Smriti Dasari ◽  
Marc Ackermann ◽  
Emily Patnaude ◽  
Stephanie OLeary ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Trials ◽  
Language Processing ◽  
Time Trial ◽  
High Fidelity ◽  
Learning Technology ◽  
Precision Oncology ◽  
Sequencing Data ◽  
Clinical Criteria ◽  
Patient Table

e13588 Background: Pre-screening for clinical trials is becoming more challenging as inclusion/exclusion criteria becomes increasingly complex. Oncology precision medicine provides an exciting opportunity to simplify this process and quickly match patients with trials by leveraging machine learning technology. The Tempus TIME Trial site network matches patients to relevant, open, and recruiting clinical trials, personalized to each patient’s clinical and molecular biology. Methods: Tempus screens patients at sites within the TIME Trial Network to find high-fidelity matches to clinical trials. The patient records include documentation submitted alongside NGS orders as well as electronic medical records (EMR) ingested through EMR Integrations. While Tempus-sequenced patients were automatically matched to trials using a Tempus-built matching application, EMR records were run through a natural language processing (NLP) data abstraction model to identify patients with an actionable gene of interest. Structured data were analyzed to filter to patients that lack a deceased date and have an encounter date within a predefined time period. Tempus abstractors manually validated the resulting unstructured records to ensure each patient was matched to a TIME Trial at a site capable of running the trial. For all high-level patient matches, a Tempus Clinical Navigator manually evaluated other clinical criteria to confirm trial matches and communicated with the site about trial options. Results: Patient matching was accelerated by implementing NLP gene and report detection (which isolated 17% of records) and manual screening. As a result, Tempus facilitated screening of over 190,000 patients efficiently using proprietary NLP technology to match 332 patients to 21 unique interventional clinical trials since program launch. Tempus continues to optimize its NLP models to increase high-fidelity trial matching at scale. Conclusions: The TIME Trial Network is an evolving, dynamic program that efficiently matches patients with clinical trial sites using both EMR and Tempus sequencing data. Here, we show how machine learning technology can be utilized to efficiently identify and recruit patients to clinical trials, thereby personalizing trial enrollment for each patient.[Table: see text]

Design-Oriented Multifidelity Fluid Simulation Using Machine Learned Fidelity Mapping

2019 ◽  
Author(s):  
Kazuko Fuchi ◽  
Eric M. Wolf ◽  
David S. Makhija ◽  
Nathan A. Wukie ◽  
Christopher R. Schrock ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Fluid Simulation ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Supervised Machine Learning ◽  
High Fidelity ◽  
Computational Domain ◽  
Symmetry Properties ◽  
High Fidelity Simulations

Abstract A machine learning algorithm that performs multifidelity domain decomposition is introduced. While the design of complex systems can be facilitated by numerical simulations, the determination of appropriate physics couplings and levels of model fidelity can be challenging. The proposed method automatically divides the computational domain into subregions and assigns required fidelity level, using a small number of high fidelity simulations to generate training data and low fidelity solutions as input data. Unsupervised and supervised machine learning algorithms are used to correlate features from low fidelity solutions to fidelity assignment. The effectiveness of the method is demonstrated in a problem of viscous fluid flow around a cylinder at Re ≈ 20. Ling et al. built physics-informed invariance and symmetry properties into machine learning models and demonstrated improved model generalizability. Along these lines, we avoid using problem dependent features such as coordinates of sample points, object geometry or flow conditions as explicit inputs to the machine learning model. Use of pointwise flow features generates large data sets from only one or two high fidelity simulations, and the fidelity predictor model achieved 99.5% accuracy at training points. The trained model was shown to be capable of predicting a fidelity map for a problem with an altered cylinder radius. A significant improvement in the prediction performance was seen when inputs are expanded to include multiscale features that incorporate neighborhood information.

scholarly journals Quantum generative adversarial learning in a superconducting quantum circuit

2019 ◽  
Vol 5 (1) ◽  
pp. eaav2761 ◽  
Author(s):  
Ling Hu ◽  
Shu-Hao Wu ◽  
Weizhou Cai ◽  
Yuwei Ma ◽  
Xianghao Mu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantum Circuit ◽  
High Fidelity ◽  
Adversarial Learning ◽  
Classical Counterpart ◽  
Learning Tasks ◽  
Challenging Tasks ◽  
Data Output ◽  
Quantum Version ◽  
Channel Simulator

Generative adversarial learning is one of the most exciting recent breakthroughs in machine learning. It has shown splendid performance in a variety of challenging tasks such as image and video generation. More recently, a quantum version of generative adversarial learning has been theoretically proposed and shown to have the potential of exhibiting an exponential advantage over its classical counterpart. Here, we report the first proof-of-principle experimental demonstration of quantum generative adversarial learning in a superconducting quantum circuit. We demonstrate that, after several rounds of adversarial learning, a quantum-state generator can be trained to replicate the statistics of the quantum data output from a quantum channel simulator, with a high fidelity (98.8% on average) so that the discriminator cannot distinguish between the true and the generated data. Our results pave the way for experimentally exploring the intriguing long-sought-after quantum advantages in machine learning tasks with noisy intermediate–scale quantum devices.

scholarly journals Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

2020 ◽  
Vol 10 (15) ◽  
pp. 5135
Author(s):  
Nuria Caballé-Cervigón ◽  
José L. Castillo-Sequera ◽  
Juan A. Gómez-Pulido ◽  
José M. Gómez-Pulido ◽  
María L. Polo-Luque
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
English Language ◽  
Data Extraction ◽  
Real Life ◽  
Human Diseases ◽  
Machine Learning Techniques ◽  
Comprehensive Overview ◽  
Advantages And Disadvantages ◽  
Journal Citation Reports

Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.

scholarly journals Machine Learning-Based Probabilistic Lithofacies Prediction from Conventional Well Logs: A Case from the Umiat Oil Field of Alaska

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4862
Author(s):  
Nilesh Dixit ◽  
Paul McColgan ◽  
Kimberly Kusler
Keyword(s):  
Machine Learning ◽  
Oil Field ◽  
Machine Learning Algorithms ◽  
Self Organizing Maps ◽  
Log Data ◽  
Core Data ◽  
Core Samples ◽  
Resolution Graph ◽  
Reservoir Facies ◽  
Self Organizing

A good understanding of different rock types and their distribution is critical to locate oil and gas accumulations in the subsurface. Traditionally, rock core samples are used to directly determine the exact rock facies and what geological environments might be present. Core samples are often expensive to recover and, therefore, not always available for each well. Wireline logs provide a cheaper alternative to core samples, but they do not distinguish between various rock facies alone. This problem can be overcome by integrating limited core data with largely available wireline log data with machine learning. Here, we presented an application of machine learning in rock facies predictions based on limited core data from the Umiat Oil Field of Alaska. First, we identified five sandstone reservoir facies within the Lower Grandstand Member using core samples and mineralogical data available for the Umiat 18 well. Next, we applied machine learning algorithms (ascendant hierarchical clustering, self-organizing maps, artificial neural network, and multi-resolution graph-based clustering) to available wireline log data to build our models trained with core-driven information. We found that self-organizing maps provided the best result among other techniques for facies predictions. We used the best self-organizing maps scheme for predicting similar reservoir facies in nearby uncored wells—Umiat 23H and SeaBee-1. We validated our facies prediction results for these wells with observed seismic data.

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