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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  
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

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
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

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

Simon Bray, Steffen Wolf
2022 arXiv   pre-print
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

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  
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

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
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

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  
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

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  
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

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  
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

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  
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

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  
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

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  
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

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  
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

Scalable machine learning-assisted model exploration and inference using Sciope

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

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  
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

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  
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
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