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Robust physics discovery via supervised and unsupervised pattern recognition using the Euler characteristic [article]

Zhiming Zhang, Yongming Liu
2021 arXiv   pre-print
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]

Grace C.Y. Peng, Mark Alber, Adrian Buganza Tepole, William Cannon, Suvranu De, Salvador Dura-Bernal, Krishna Garikipati, George Karniadakis, William W. Lytton, Paris Perdikaris, Linda Petzold, Ellen Kuhl
2020 arXiv   pre-print
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]

Niket Sharma, Y. A. Liu
2021 arXiv   pre-print
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

Andrzej Skowron
2006 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology  
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

Andrzej Skowron
2006 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06)  
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

Mark Alber, Adrian Buganza Tepole, William R. Cannon, Suvranu De, Salvador Dura-Bernal, Krishna Garikipati, George Karniadakis, William W. Lytton, Paris Perdikaris, Linda Petzold, Ellen Kuhl
2019 npj Digital Medicine  
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

Henry Chan, Mathew Cherukara, Troy D. Loeffler, Badri Narayanan, Subramanian K. R. S. Sankaranarayanan
2020 npj Computational Materials  
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

Valeriy Gavrishchaka, Olga Senyukova, Mark Koepke
2019 Advances in Physics: X  
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

Varshika Singh, Santanu Patra, Natarajan Arul Murugan, Dana-Cristina Toncu, Ashutosh Tiwari
2022 Materials Advances  
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]

Marta D'Elia, Hang Deng, Cedric Fraces, Krishna Garikipati, Lori Graham-Brady, Amanda Howard, George Karniadakis, Vahid Keshavarzzadeh, Robert M. Kirby, Nathan Kutz, Chunhui Li, Xing Liu (+12 others)
2022 arXiv   pre-print
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

Guang-ren Shi, Xin-She Yang
2010 Procedia Computer Science  
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

Nima Bari, Roman Vichr, Kamran Kowsari, Simon Berkovich
2014 2014 International Conference on Data Science and Advanced Analytics (DSAA)  
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

Michael R. Berthold, Paul R. Cohen, Xiaohui Liu
1998 The AI Magazine  
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]

Feng Li, Jiangxin Wang, Lei Nie, Weiwen Zhang
2012 Metabolomics  
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

Chung-Horng Lung, Joseph E. Urban, Gerald T. Mackulak
2006 Requirements Engineering  
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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