Physics Informed Machine Learning For Time Domain Electromagnetic Simulation

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dc.contributor.advisorChen, Jiefu
dc.contributor.committeeMemberWu, Xuqing
dc.contributor.committeeMemberJackson, David R.
dc.creatorSu, Yawei
dc.creator.orcid0000-0003-4785-5893
dc.date.accessioned2023-01-01T23:07:48Z
dc.date.createdAugust 2022
dc.date.issued2022-08-15
dc.date.updated2023-01-01T23:07:49Z
dc.description.abstractNeural Networks have been widely used in all fields of research and have achieved great success. The Physics Informed Neural Network (PINN) is an important and relatively new attempt among deep neural network applications. PINN is built upon a differentiable programming platform and aims at solving differential equations. PINN provides an alternative way to solve physics problems apart from conventional numerical methods like Finite-Difference Time-Domain (FDTD) method or Finite Element Analysis (FEA), and turns the solving of equations to an optimization process. In this thesis paper, I follow the work of Pan Zhang and test PINNs on several time-domain electromagnetic simulation examples. I concentrate on a simple 1D electromagnetic cavity model with isotropic and homogeneous media without current sources. During the experiment, I extend the training time range to investigate the PINN performance on large time-scale problems. We do find that PINN is less likely to converge to an expected physical solution when the time scale gets larger and finally fails to produce any meaningful results, which is known as the “Spectral Bias”. To further understand this problem, we firstly define a parameter “Threshold Period Number” (TPN) and tune the PINN parameters to see how these values influence the TPN. Then we change and improve the PINN structure, trying to avoid the failure for the large time-scale problem. We also visualize the PINN training process to help us analyze the problem. Although we still can not totally solve the spectral bias, we find out some effective methods to practically increase TPN and get better results within certain large time scales.
dc.description.departmentElectrical and Computer Engineering, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10657/13254
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectMachine learning
dc.subjectPhysics Informed Neural Network
dc.subjectPINN
dc.subjectMaxwell Equation
dc.subjectSpectral Bias
dc.subjectThreshold Period Number
dc.titlePhysics Informed Machine Learning For Time Domain Electromagnetic Simulation
dc.type.dcmiText
dc.type.genreThesis
dcterms.accessRightsThe full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period.
local.embargo.lift2024-08-01
local.embargo.terms2024-08-01
thesis.degree.collegeCullen College of Engineering
thesis.degree.departmentElectrical and Computer Engineering, Department of
thesis.degree.disciplineElectrical Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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