Machine learning for data-driven discovery in solid Earth geoscience

Science ◽  
2019 ◽  
Vol 363 (6433) ◽  
pp. eaau0323 ◽  
Author(s):  
Karianne J. Bergen ◽  
Paul A. Johnson ◽  
Maarten V. de Hoop ◽  
Gregory C. Beroza
Keyword(s):  
Machine Learning ◽  
Solid Earth ◽  
Computer Simulations ◽  
Direct Observation ◽  
The State ◽  
Important Task ◽  
Data Availability ◽  
Data Driven ◽  
State Of The Field

Understanding the behavior of Earth through the diverse fields of the solid Earth geosciences is an increasingly important task. It is made challenging by the complex, interacting, and multiscale processes needed to understand Earth’s behavior and by the inaccessibility of nearly all of Earth’s subsurface to direct observation. Substantial increases in data availability and in the increasingly realistic character of computer simulations hold promise for accelerating progress, but developing a deeper understanding based on these capabilities is itself challenging. Machine learning will play a key role in this effort. We review the state of the field and make recommendations for how progress might be broadened and accelerated.

scholarly journals Predicting the state of charge and health of batteries using data-driven machine learning

2020 ◽  
Vol 2 (3) ◽  
pp. 161-170 ◽  
Author(s):  
Man-Fai Ng ◽  
Jin Zhao ◽  
Qingyu Yan ◽  
Gareth J. Conduit ◽  
Zhi Wei Seh
Keyword(s):  
Machine Learning ◽  
The State ◽  
State Of Charge ◽  
Data Driven ◽  
Using Data

scholarly journals Practices and Infrastructures for ML Systems – An Interview Study

2021 ◽  
Author(s):  
Dennis Muiruri ◽  
Lucy Ellen Lwakatare ◽  
Jukka K. Nurminen ◽  
Tommi Mikkonen
Keyword(s):  
Machine Learning ◽  
Best Practices ◽  
Data Management ◽  
Management Practices ◽  
Interview Study ◽  
The State ◽  
Data Driven ◽  
Software Systems ◽  
State Of Practice ◽  
Model Training

<div> <div> <div> <p>The best practices and infrastructures for developing and maintaining machine learning (ML) enabled software systems are often reported by large and experienced data-driven organizations. However, little is known about the state of practice across other organizations. Using interviews, we investigated practices and tool-chains for ML-enabled systems from 16 organizations in various domains. Our study makes three broad observations related to data management practices, monitoring practices and automation practices in ML model training, and serving workflows. These have limited number of generic practices and tools applicable across organizations in different domains. </p> </div> </div> </div>

scholarly journals LOCALIZATION OF SOIL MONITORING DATA PROCESSING

2021 ◽  
pp. 22-25
Author(s):  
V. Fartukov ◽  
N. Hanov
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Data Processing ◽  
The State ◽  
Monitoring Data ◽  
Soil Monitoring ◽  
Monitoring Data Processing ◽  
State Of The Field

A tree of data analysis for the formation and preprocessing, storage and protection of data based on Big Data and Blockchain technologies has been developed. The developed algorithm allows for the classification of data on the state of the field, split testing of data, forecasting and machine learning for the implementation of differential irrigation with sprinklers.

scholarly journals Targeted sequence design within the coarse-grained polymer genome

2020 ◽  
Vol 6 (43) ◽  
pp. eabc6216
Author(s):  
Michael A. Webb ◽  
Nicholas E. Jackson ◽  
Phwey S. Gil ◽  
Juan J. de Pablo
Keyword(s):  
Machine Learning ◽  
Materials Science ◽  
Chain Structure ◽  
Data Availability ◽  
Data Driven ◽  
Coarse Grained ◽  
Grand Challenge ◽  
Sequence Information ◽  
Single Chain ◽  
Sequence Design

The chemical design of polymers with target structural and/or functional properties represents a grand challenge in materials science. While data-driven design approaches are promising, success with polymers has been limited, largely due to limitations in data availability. Here, we demonstrate the targeted sequence design of single-chain structure in polymers by combining coarse-grained modeling, machine learning, and model optimization. Nearly 2000 unique coarse-grained polymers are simulated to construct and analyze machine learning models. We find that deep neural networks inexpensively and reliably predict structural properties with limited sequence information as input. By coupling trained ML models with sequential model-based optimization, polymer sequences are proposed to exhibit globular, swollen, or rod-like behaviors, which are verified by explicit simulations. This work highlights the promising integration of coarse-grained modeling with data-driven design and represents a necessary and crucial step toward more complex polymer design efforts.

scholarly journals Machine learning and fault rupture: a review

2020 ◽  
Author(s):  
Christopher Ren ◽  
Claudia Hulbert ◽  
Paul Johnson ◽  
Bertrand Rouet‐Leduc
Keyword(s):  
Machine Learning ◽  
Solid Earth ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Fault Rupture ◽  
Exponential Increase ◽  
Recent Advances ◽  
Learning Techniques

Geophysics has historically been a data-driven field, however in recent years the exponential increase of available data has lead to increased adoption of machine learning techniques and algorithm for analysis, detection and forecasting applications to faulting. This work reviews recent advances in the application of machine learning in the study of fault rupture ranging from the laboratory to Solid Earth.

scholarly journals Practices and Infrastructures for ML Systems – An Interview Study

2021 ◽  
Author(s):  
Dennis Muiruri ◽  
Lucy Ellen Lwakatare ◽  
Jukka K. Nurminen ◽  
Tommi Mikkonen
Keyword(s):  
Machine Learning ◽  
Best Practices ◽  
Data Management ◽  
Management Practices ◽  
Interview Study ◽  
The State ◽  
Data Driven ◽  
Software Systems ◽  
State Of Practice ◽  
Model Training

<div> <div> <div> <p>The best practices and infrastructures for developing and maintaining machine learning (ML) enabled software systems are often reported by large and experienced data-driven organizations. However, little is known about the state of practice across other organizations. Using interviews, we investigated practices and tool-chains for ML-enabled systems from 16 organizations in various domains. Our study makes three broad observations related to data management practices, monitoring practices and automation practices in ML model training, and serving workflows. These have limited number of generic practices and tools applicable across organizations in different domains. </p> </div> </div> </div>

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