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Simulation assisted machine learning

Timo M Deist, Andrew Patti, Zhaoqi Wang, David Krane, Taylor Sorenson, David Craft, Oliver Stegle
2019 Bioinformatics  

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2018 pre-print arXiv:1802.05688v2 fatcat:2am544q2bfbmzgsmsi3l4gs4hq

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2019 accepted
arXiv:1802.05688v4 fatcat:dvktk4t77fbvfovaziebqltt3q
2018 accepted
arXiv:1802.05688v3 fatcat:pyqt2kwqljcqli7qwnv5vrofte
2018 pre-print
arXiv:1802.05688v1 fatcat:6q5sm5iy2rdvfba6iz5xnvslbe
In this context we propose using approximate simulations to build a kernel for use in kernelized machine learning methods, such as support vector machines.  ...  When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs.  ...  that typically affects genomic machine learning.  ... 
doi:10.1093/bioinformatics/btz199 pmid:30903692 pmcid:PMC6792064 fatcat:hnhozb6prne2pjlicswrq6kh24
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Citation

Timo M Deist, Andrew Patti, Zhaoqi Wang, David Krane, Taylor Sorenson, David Craft, Oliver Stegle. "Simulation assisted machine learning." Bioinformatics 35.20 (2019) 4072-4080

Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks [article]

Francesc Wilhelmi, Marc Carrascosa, Cristina Cano, Anders Jonsson, Vishnu Ram, Boris Bellalta
2021 arXiv   pre-print

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2020 pre-print
arXiv:2005.08281v1 fatcat:7f6rjufhubbctdq5kqvfaatpny
Without any doubt, Machine Learning (ML) will be an important driver of future communications due to its foreseen performance when applied to complex problems.  ...  Finally, we illustrate the integration of network simulators into ML-assisted communications through a proof-of-concept testbed implementation of a residential Wi-Fi network.  ...  Machine-Learning-based Transmit Power Control To improve the performance of the target WLAN, we simulate a Multi-Armed Bandits (MABs) mechanism for Transmit Power Control (TPC), as previously done in  ... 
arXiv:2005.08281v2 fatcat:4g7zchvkazakbjuvzy62h37qsa
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Francesc Wilhelmi, Marc Carrascosa, Cristina Cano, Anders Jonsson, Vishnu Ram, Boris Bellalta. "Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks." arXiv (2021)

Non-equilibrium biomolecular simulation pathway analysis assisted by machine learning and graph methods [article]

Simon Bray, Steffen Wolf
2022 arXiv   pre-print

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The first approach is based on a contact principal component analysis for reducing the dimensionality of the input data, followed by identification of pathways and training a machine learning model for  ...  Analysis of such simulations therefore requires detection of paths, as well as of reaction coordinates the paths appear in.  ...  If it is possible, on this basis, to identify clearly separated pathways, a machine-learning model should be built to classify trajectories.  ... 
arXiv:2205.09894v1 fatcat:dax72rndmnhg3fjfqgakccovum
Citation

Simon Bray, Steffen Wolf. "Non-equilibrium biomolecular simulation pathway analysis assisted by machine learning and graph methods." arXiv (2022)

Machine Learning-Assisted High-Throughput Molecular Dynamics Simulation of High-Mechanical Performance Carbon Nanotube Structure

Yi Xiang, Koji Shimoyama, Keiichi Shirasu, Go Yamamoto
2020 Nanomaterials  

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By using machine learning-assisted high-throughput molecular dynamics (HTMD) simulation, the relationship among the following structural parameters/properties was investigated: diameter, number of walls  ...  A database, comprising the various tensile test simulation results, was analyzed using a self-organizing map (SOM).  ...  Kikugawa of the Institute of Fluid Science, Tohoku University for technical assistance in CNT modeling and MD calculations. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/nano10122459 pmid:33316937 fatcat:pspcn32bnjgflhtqlyyxjfopeq
DOAJ
Citation

Yi Xiang, Koji Shimoyama, Keiichi Shirasu, Go Yamamoto. "Machine Learning-Assisted High-Throughput Molecular Dynamics Simulation of High-Mechanical Performance Carbon Nanotube Structure." Nanomaterials 10.12 (2020) 2459

Machine Learning-Assisted Multi-Objective Optimization of Battery Manufacturing from Synthetic Data Generated by Physics-Based Simulations [article]

Marc Duquesnoy, Chaoyue Liu, Diana Zapata Dominguez, Vishank Kumar, Elixabete Ayerbe, Alejandro A. Franco
2022 arXiv   pre-print

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In this work, we tackle this issue by proposing an innovative approach, supported by a deterministic machine learning (ML)-assisted pipeline for multi-objective optimization of LIB electrode properties  ...  Firstly, the pipeline generates a synthetic dataset from physics-based simulations with low discrepancy sequences, that allow to sufficiently represent the manufacturing parameters space.  ...  This digitalization is expected to be supported on physics-based and machine learning (ML) modeling simulating each step of the manufacturing process and their interlinks.  ... 
arXiv:2205.01621v1 fatcat:azkeomlhijemxh4vuwies5nqte
Open Access
Citation

Marc Duquesnoy, Chaoyue Liu, Diana Zapata Dominguez, Vishank Kumar, Elixabete Ayerbe, Alejandro A. Franco. "Machine Learning-Assisted Multi-Objective Optimization of Battery Manufacturing from Synthetic Data Generated by Physics-Based Simulations." arXiv (2022)

A Machine-Learning-Assisted Simulation Approach for Incorporating Predictive Maintenance in Dynamic Flow-Shop Scheduling

Eman Azab, Mohamed Nafea, Lamia A. Shihata, Maggie Mashaly
2021 Applied Sciences  

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In this paper, a machine-learning-assisted simulation approach for dynamic flow-shop production scheduling is proposed.  ...  Several machine-learning algorithms are tested and compared to see which one provides the highest accuracy.  ...  Machine-Learning Results The machine-learning results would first be tackled since that it is needed to determine the PdM slots for the production schedule before the use of DES.  ... 
doi:10.3390/app112411725 fatcat:vgmr2tobwvccfmh5hhr5llwkwy
DOAJ
Citation

Eman Azab, Mohamed Nafea, Lamia A. Shihata, Maggie Mashaly. "A Machine-Learning-Assisted Simulation Approach for Incorporating Predictive Maintenance in Dynamic Flow-Shop Scheduling." Applied Sciences 11.24 (2021) 11725

Probing the Stochastic Dynamics of Coronaviruses: Machine Learning Assisted Deep Computational Insights with Exploitable Dimensions

T. Mukhopadhyay, S. Naskar, K. K. Gupta, R. Kumar, S. Dey, S. Adhikari
2021 Advanced Theory and Simulations  

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A machine learning assisted efficient, yet comprehensive characterization of the dynamics of coronaviruses, in conjunction with finite element (FE) approach, is presented.  ...  To quantify the inherent system-uncertainty efficiently, Monte Carlo simulation is proposed in conjunction with the machine learning based FE computational framework for obtaining complete probabilistic  ...  Figure 4 . 4 Machine learning assisted rapid dynamic analysis of coronavirus.  ... 
doi:10.1002/adts.202000291 fatcat:54qjwsf34ndspn5cznq3yo4sbm
Open Access
Citation

T. Mukhopadhyay, S. Naskar, K. K. Gupta, R. Kumar, S. Dey, S. Adhikari. "Probing the Stochastic Dynamics of Coronaviruses: Machine Learning Assisted Deep Computational Insights with Exploitable Dimensions." Advanced Theory and Simulations 4.7 (2021) 2000291

How machine learning can assist the interpretation of ab initio molecular dynamics simulations and conceptual understanding of chemistry

Florian Häse, Ignacio Fdez. Galván, Alán Aspuru-Guzik, Roland Lindh, Morgane Vacher
2019 Chemical Science  

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Machine learning models, trained to reproduce molecular dynamics results, help interpreting simulations and extracting new understanding of chemistry.  ...  It is rather on training a machine learning model on already simulated trajectories (of a given chemical reaction and at a given level of theory), and interpreting and using the trained machine learning  ...  It also shows how machine learning models help interpreting the results of molecular dynamics simulations.  ... 
doi:10.1039/c8sc04516j pmid:30881655 pmcid:PMC6385677 fatcat:zuyo5tfbyrgfbghtd666k4ktlq
DOAJ
Citation

Florian Häse, Ignacio Fdez. Galván, Alán Aspuru-Guzik, Roland Lindh, Morgane Vacher. "How machine learning can assist the interpretation of ab initio molecular dynamics simulations and conceptual understanding of chemistry." Chemical Science 10.8 (2019) 2298-2307

Boundary Conditions for Simulations of Fluid Flow and Temperature Field during Ammonothermal Crystal Growth—A Machine-Learning Assisted Study of Autoclave Wall Temperature Distribution

Saskia Schimmel, Daisuke Tomida, Makoto Saito, Quanxi Bao, Toru Ishiguro, Yoshio Honda, Shigefusa Chichibu, Hiroshi Amano
2021 Crystals  

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Machine learning is applied to efficiently determine the power boundary conditions needed to obtain set temperatures at specified locations.  ...  Thermal boundary conditions for numerical simulations of ammonothermal GaN crystal growth are investigated.  ...  Figure 2 . 2 Workflow using for machine-learning assisted power adjustment in simulations. Figure 2 . 2 Workflow using for machine-learning assisted power adjustment in simulations.  ... 
doi:10.3390/cryst11030254 fatcat:635qtdbvdnfn7ivuhbavf65u64
DOAJ
Citation

Saskia Schimmel, Daisuke Tomida, Makoto Saito, Quanxi Bao, Toru Ishiguro, Yoshio Honda, Shigefusa Chichibu, Hiroshi Amano. "Boundary Conditions for Simulations of Fluid Flow and Temperature Field during Ammonothermal Crystal Growth—A Machine-Learning Assisted Study of Autoclave Wall Temperature Distribution." Crystals (2021) 254

Combining Machine Learning and Simulation to a Hybrid Modelling Approach: Current and Future Directions [chapter]

Laura von Rueden, Sebastian Mayer, Rafet Sifa, Christian Bauckhage, Jochen Garcke
2020 Lecture Notes in Computer Science  

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machine learning and machine-learning assisted simulation.  ...  In this paper, we describe the combination of machine learning and simulation towards a hybrid modelling approach.  ...  Machine-learning assisted simulation describes the integration of machine learning into simulation. 3.  ... 
doi:10.1007/978-3-030-44584-3_43 fatcat:s3ycww2tq5c6pgos4qj2y27a54
Citation

Laura von Rueden, Sebastian Mayer, Rafet Sifa, Christian Bauckhage, Jochen Garcke. "Combining Machine Learning and Simulation to a Hybrid Modelling Approach: Current and Future Directions." Lecture Notes in Computer Science (2020) 548-560

The Virtual Operative Assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine

Nykan Mirchi, Vincent Bissonnette, Recai Yilmaz, Nicole Ledwos, Alexander Winkler-Schwartz, Rolando F. Del Maestro, Paweł Pławiak
2020 PLoS ONE  

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The application of artificial intelligence and machine learning technologies has provided new methodologies to utilize large amounts of data for educational purposes.  ...  This study aims to 1) introduce a new framework using explainable artificial intelligence for simulation-based training in surgery, and 2) validate the framework by creating the Virtual Operative Assistant  ...  [12] Machine learning algorithm training & testing Once final metrics have been selected, they can be used to train supervised machine learning algorithms.  ... 
doi:10.1371/journal.pone.0229596 pmid:32106247 pmcid:PMC7046231 fatcat:itidobaenzgtzdimyqg7hqwlgq
DOAJ
Citation

Nykan Mirchi, Vincent Bissonnette, Recai Yilmaz, Nicole Ledwos, Alexander Winkler-Schwartz, Rolando F. Del Maestro, Paweł Pławiak. "The Virtual Operative Assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine." PLoS ONE 15.2 (2020) e0229596

Using machine learning techniques to interpret results from discrete event simulation [chapter]

Dunja Mladenić, Ivan Bratko, Ray J. Paul, Marko Grobelnik
1994 Lecture Notes in Computer Science  

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The results of two simulators were processed as machine learning problems.  ...  This paper describes an approach to the interpretation of discrete event simulation results using machine learning techniques.  ...  Section 2 describes discrete event simulation domains used in our experiments with machine learning.  ... 
doi:10.1007/3-540-57868-4_83 fatcat:igyxjx23zzdm7f2xr2flikiv5m
Citation

Dunja Mladenić, Ivan Bratko, Ray J. Paul, Marko Grobelnik. "Using machine learning techniques to interpret results from discrete event simulation." Lecture Notes in Computer Science (1994) 399-402

Scalable machine learning-assisted model exploration and inference using Sciope

Prashant Singh, Fredrik Wrede, Andreas Hellander, Pier Luigi Martelli
2020 Bioinformatics  

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Recently, machine-learning assisted methods have shown great promise to handle larger, more complex models.  ...  Sciope is designed to support new algorithms for machine-learning assisted model exploration and likelihood-free inference.  ...  Machine-learning-assisted methods have been proposed to tackle this problem with a data-driven approach to both exploration and likelihood-free inference.  ... 
doi:10.1093/bioinformatics/btaa673 pmid:32706854 pmcid:PMC8055224 fatcat:nnssvkymv5hfrlphxoqscaavaq
Citation

Prashant Singh, Fredrik Wrede, Andreas Hellander, Pier Luigi Martelli. "Scalable machine learning-assisted model exploration and inference using Sciope." Bioinformatics 37.2 (2020) 279-281

A strategy for quantum algorithm design assisted by machine learning

Jeongho Bang, Junghee Ryu, Seokwon Yoo, Marcin Pawłowski, Jinhyoung Lee
2014 New Journal of Physics  

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2014 accepted
arXiv:1301.1132v4 fatcat:rcbhdqmi6rbivddtyqvb2gxkku
2013 pre-print
arXiv:1301.1132v1 fatcat:fixqcjff3fbynef56wm4qpmwia
2013 pre-print
arXiv:1301.1132v2 fatcat:byxbtrn2gba37ohlbbbgmlb54y
2014 pre-print
arXiv:1301.1132v3 fatcat:b4bnfz27lngqxifk6dc2t2zrmu
We propose a method for quantum algorithm design assisted by machine learning.  ...  We showed by using Monte-Carlo simulations that our simulator can faithfully learn quantum algorithm to solve the problem for given oracle.  ...  This classical machine can thus be regarded as a simulator that learns a quantum algorithm-a so-called learning simulator.  ... 
doi:10.1088/1367-2630/16/7/073017 fatcat:zvrrq5u255ddbfv7hw2wmwr7xy
DOAJ Multiple Versions
Citation

Jeongho Bang, Junghee Ryu, Seokwon Yoo, Marcin Pawłowski, Jinhyoung Lee. "A strategy for quantum algorithm design assisted by machine learning." New Journal of Physics 16.7 (2014) 073017

Learning Factory Modules for Smart Factories in Industrie 4.0

Christopher Prinz, Friedrich Morlock, Sebastian Freith, Niklas Kreggenfeld, Dieter Kreimeier, Bernd Kuhlenkötter
2016 Procedia CIRP  

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Workplace-related scenarios can be mapped through practical learning. This proceeding enables participants to transfer learned knowledge directly to the own workplace.  ...  It describes the new job profile of employees in Industrie 4.0 and thoroughly discusses the various learning modules with their individual learning targets and mapped scenarios.  ...  In order to give intelligent assistance, digital services need more information about the process (operating process, maintenance process, machine data, production data, human-machine interactions, machine  ... 
doi:10.1016/j.procir.2016.05.105 fatcat:kkwslcionbbfhahxtnmnueruu4
Open Access
Citation

Christopher Prinz, Friedrich Morlock, Sebastian Freith, Niklas Kreggenfeld, Dieter Kreimeier, Bernd Kuhlenkötter. "Learning Factory Modules for Smart Factories in Industrie 4.0." Procedia CIRP (2016) 113-118

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