DC Field | Value | Language |
---|---|---|
dc.contributor.author | 권희재 | - |
dc.date.accessioned | 2023-08-31T16:34:58Z | - |
dc.date.available | 2023-08-31T16:34:58Z | - |
dc.date.issued | 2023 | - |
dc.identifier.other | OAK-2015-10205 | - |
dc.identifier.uri | http://postech.dcollection.net/common/orgView/200000690586 | ko_KR |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/118402 | - |
dc.description | Master | - |
dc.description.abstract | In recent years, physics-informed neural networks(PINN) are based on deep learning and have been widely used in scientific and engineering problems, where differential equations serve as the foundation. In our case, we are focusing on the field of plasma, specifically the chlorine discharge global model. The model is composed of a set of stiffness ordinary differential equations obtained from various chemical reaction equations. However, the naive-PINNs has limitations in addressing stiffness. Therefore, in this paper, we aim to address stiffness by combining selected methodologies from the fields of both deep learning that enhances the efficiency of learning(log transformation, standardization) and various PINNs methodologies(Fourier embedding, self-adaptive learning). We demonstrate the effectiveness of the integrated methodology through the ROBER problem, a simple argon stiff ordinary differential equation, and ultimately, the chlorine discharge global model which is of our interest. | - |
dc.language | eng | - |
dc.publisher | 포항공과대학교 | - |
dc.title | Modeling Chemical Reaction ODEs using PINNs | - |
dc.title.alternative | 물리 정보 신경망을 사용한 화학 반응 ODE 모델링 : 플라즈마 방전 모델 | - |
dc.type | Thesis | - |
dc.contributor.college | 수학과 | - |
dc.date.degree | 2023- 8 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
library@postech.ac.kr Tel: 054-279-2548
Copyrights © by 2017 Pohang University of Science ad Technology All right reserved.