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Robust physics discovery via supervised and unsupervised pattern recognition using the Euler characteristic
[article]
2021
arXiv
pre-print
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In this study, we use an efficient topological descriptor for complex data, i.e., the Euler characteristics (ECs), as features to characterize the spatiotemporal data collected from dynamical systems and ...
Machine learning approaches have been widely used for discovering the underlying physics of dynamical systems from measured data. ...
Acknowledgements The research reported in this paper was supported by funds from NASA University Leadership Initiative Program (Contract No. NNX17AJ86A, Project Officer: Dr. ...
arXiv:2110.13610v1
fatcat:vz3jqscduzgdzbgz7bm23nfxdm
Multiscale modeling meets machine learning: What can we learn?
[article]
2020
arXiv
pre-print
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With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions ...
in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create ...
The identification of the system of governing equations still leaves open the question of efficiency and time to solution, especially if equations are to be used in a sampling approach such as Monte Carlo ...
arXiv:1911.11958v2
fatcat:u4d4snmwq5bfrcvt7is6fkswgy
A Hybrid Science-Guided Machine Learning Approach for Modeling and Optimizing Chemical Processes
[article]
2021
arXiv
pre-print
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We present a detailed review of scientific and engineering literature relating to the hybrid SGML approach, and propose a systematic classification of hybrid SGML models. ...
This study presents a broad perspective of hybrid process modeling and optimization combining the scientific knowledge and data analytics in bioprocessing and chemical engineering with a science-guided ...
., for their support of the Center of Excellence in Process System Engineering in the Department of Chemical Engineering at Virginia Tech. We would like to thank Dr. ...
arXiv:2112.01475v1
fatcat:qvcoyiut3redfptvpg42qc2hte
Approximate Reasoning in MAS: Rough Set Approach
2006
2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology
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Acknowledgements I would like to express gratitude to Dr. Andrzej Jankowski for cooperation on wistech. ...
The research has been supported by the grant 3 T11C 002 26 from Ministry of Scientific Research and Information Technology of the Republic of Poland. ...
Another application of hierarchical learning for sanspot classification is reported in [40] . ...
doi:10.1109/iat.2006.38
dblp:conf/iat/Skowron06
fatcat:hceu4eympjem7cltvv4ipc73oa
Approximate Reasoning in MAS: Rough Set Approach
2006
2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06)
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Acknowledgements I would like to express gratitude to Dr. Andrzej Jankowski for cooperation on wistech. ...
The research has been supported by the grant 3 T11C 002 26 from Ministry of Scientific Research and Information Technology of the Republic of Poland. ...
Another application of hierarchical learning for sanspot classification is reported in [40] . ...
doi:10.1109/wi.2006.43
fatcat:ey4peluof5aorkc7hvzvwxmovi
Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences
2019
npj Digital Medicine
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equations, partial differential equations, data-driven approaches, and theory-driven approaches. ...
The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this ...
For example, it seems intuitive to a priori build physicsbased knowledge in the form of partial differential equations, boundary conditions, and constraints into a machine learning approach. 22 Especially ...
doi:10.1038/s41746-019-0193-y
pmid:31799423
pmcid:PMC6877584
fatcat:uhgdhq7rjffqnboydb3e6d2tuu
Machine learning enabled autonomous microstructural characterization in 3D samples
2020
npj Computational Materials
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To demonstrate the efficacy of our ML approach, we benchmark it against a diverse set of synthetic data samples representing nanocrystalline metals, polymers and complex fluids as well as experimentally ...
Our technique does not require a priori microstructure description of the target system and is insensitive to disorder such as extended defects in polycrystals arising from line and plane defects. ...
Local variance filter with a 90-percentile thresholding is used for grain boundary identification, which alleviates the error sensitivity of the method to different voxelization bin sizes. ...
doi:10.1038/s41524-019-0267-z
fatcat:gem6zwjqqjfp5lxtsrrj4qqewu
Synergy of physics-based reasoning and machine learning in biomedical applications: towards unlimited deep learning with limited data
2019
Advances in Physics: X
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significant data incompleteness, and boosting accuracy of low-complexity models within the classifier ensemble, as illustrated in physiological-data analysis. ...
Historical data incompleteness problem and curse of dimensionality diminish practical value of pure data-driven approaches, especially in biomedicine. ...
Disclosure statement Authors claim no potential conflict of interest exists. ...
doi:10.1080/23746149.2019.1582361
fatcat:wkmef4jmgreurnseofsaqa5dva
Recent trends in computational tools and data-driven modeling for advanced materials
2022
Materials Advances
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The paradigm of advanced materials has grown exponentially over the last decade, with their new dimensions including digital design, dynamics, and functions. ...
Acknowledgements The authors gratefully acknowledge the financial support from the International Association of Advanced Materials. ...
In a system, atoms and molecules are only allowed to interact for a particular time span, which gives a view of the dynamic evolution. ...
doi:10.1039/d2ma00067a
fatcat:2ww2zqtj6vf2ta7djtkbqyc7hm
Machine Learning in Heterogeneous Porous Materials
[article]
2022
arXiv
pre-print
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modeling in heterogeneous porous materials via ML, and Discovery of materials constitutive laws and new governing equations. ...
Within the scope of ML and materials research, the goal of the workshop was to discuss the state-of-the-art in each community, promote crosstalk and accelerate multi-disciplinary collaborative research ...
We would also like to thank the Department of Mechanical Engineering at The University of Utah for assistance with the logistics and behind the scene preparations for the workshop. ...
arXiv:2202.04137v1
fatcat:tuhghvcifnebzo2pcwifeek4vu
Optimization and data mining for fracture prediction in geosciences
2010
Procedia Computer Science
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The MRA, BPNN and SVM methods are used as optimization techniques for knowledge discovery in data. ...
We present a case study of the application of data mining and optimization in the prediction of fractures using well-logging data. ...
The application of optimization and data mining in geosciences databases can be very promising in dealing with large data sets and modelling complex systems. ...
doi:10.1016/j.procs.2010.04.151
fatcat:5a55b66lbvavviigpiig53egdm
23-bit metaknowledge template towards Big Data knowledge discovery and management
2014
2014 International Conference on Data Science and Advanced Analytics (DSAA)
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As the trend increases in popularity, the need for a highly adaptive solution for knowledge discovery will be necessary. ...
In fact, the continued improvement of the interoperability of machine learning, statistics, database building and querying fused to create this increasingly popular science- Data Mining and Knowledge Discovery ...
Acknowledgment- We would like to sincerely thank Professor Simon Berkovich for his guidance and a special thanks to our friend and collaborator Dr. Roman Vichr. ...
doi:10.1109/dsaa.2014.7058121
dblp:conf/dsaa/BariVKB14
fatcat:ol6smkndpbe6xfputsfqxdfy6m
Intelligent Data Analysis: Reasoning About Data
1998
The AI Magazine
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Special Group on Expert Systems, the Institute of Electrical and Electronics Engineers Systems, Men, and Cybernetics, and the Society for the Study of Artificial Intelligence and Simulation of Behavior ...
Thanks go to all the authors, participants, exhibitors, and invited speakers and to the program committee and auxiliary reviewers for their excellent reviews. ...
The discovery of knowledge in databases is receiving a lot of attention these days, and several such papers were presented at IDA97. D. ...
doi:10.1609/aimag.v19i4.1427
dblp:journals/aim/BertholdCL98
fatcat:pzl47k6ngre7fj2xds542okap4
Computational Methods to Interpret and Integrate Metabolomic Data
[chapter]
2012
Metabolomics
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RS has been used for the identification of microorganisms of medical relevance ; however, its application for complex biological systems outside the area of microbiology is still in its infancy, although ...
the complexity of biological systems and an integrated "omics" approach may be a key to decipher complex biological systems (Gygi et al., 1999; Zhang et al., 2010) . ...
This book will provide the reader with summaries of the state-of-the-art of technologies and methodologies, especially in the data analysis and interpretation approaches, as well as give insights into ...
doi:10.5772/32517
fatcat:ety5vgnvfnaw5evchxhgbab3wy
Analogy-based domain analysis approach to software reuse
2006
Requirements Engineering
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Software analogy can provide the analyst with similar problems and solutions to reuse previous domain analysis knowledge or artifacts for a new domain. ...
This paper presents case studies to demonstrate the increase of efficiency in applying the approach. Evaluation of the approach from various perspectives is also reported. ...
For large, complex, or newer domains, an incremental approach is advocated to gradually acquire knowledge. ...
doi:10.1007/s00766-006-0035-8
fatcat:rurjaa46x5godk6xbnfjuodfzm
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