A novel physics informed deep learning method for simulation-based modelling

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dc.contributor.authorKarali, Hasan
dc.contributor.authorDemirezen, Umut M.
dc.contributor.authorYukselen, Mahmut A.
dc.contributor.authorInalhan, Gokhan
dc.date.accessioned2021-01-28T11:05:12Z
dc.date.available2021-01-28T11:05:12Z
dc.date.issued2021-01-04
dc.description.abstractIn this paper, we present a brief review of the state of the art physics informed deep learning methodology and examine its applicability, limits, advantages, and disadvantages via several applications. The main advantage of this method is that it can predict the solution of the partial differential equations by using only boundary and initial conditions without the need for any training data or pre-process phase. Using physics informed neural network algorithms, it is possible to solve partial differential equations in many different problems encountered in engineering studies with a low cost and time instead of traditional numerical methodologies. A direct comparison between the initial results of the current model, analytical solutions, and computational fluid dynamics methods shows very good agreement. The proposed methodology provides a crucial basis for solution of more advance partial differential equation systems and offers a new analysis and mathematical modelling tool for aerospace applicationsen_UK
dc.identifier.citationKarali H, Umut Demirezen M, et al., (2021) A novel physics informed deep learning method for simulation-based modelling. In: AIAA SciTech Forum 2021, 11-15 and 19-21 January 2021, Virtual Eventen_UK
dc.identifier.urihttps://doi.org/10.2514/6.2021-0177
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16273
dc.language.isoenen_UK
dc.publisherAIAAen_UK
dc.relation.ispartofseries;AIAA 2021-0177
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectModeling and Simulation Technologiesen_UK
dc.titleA novel physics informed deep learning method for simulation-based modellingen_UK
dc.typeConference paperen_UK

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