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AI and Machine Learning for SNM Detection and Solution of PDES With Interface Conditions- [electronic resource]
AI and Machine Learning for SNM Detection and Solution of PDES With Interface Conditions- [electronic resource]
- 자료유형
- 학위논문
- Control Number
- 0016932780
- International Standard Book Number
- 9798379840877
- Dewey Decimal Classification Number
- 621
- Main Entry-Personal Name
- Lagari, Pola Lydia.
- Publication, Distribution, etc. (Imprint
- [S.l.] : Purdue University., 2022
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2022
- Physical Description
- 1 online resource(105 p.)
- General Note
- Source: Dissertations Abstracts International, Volume: 85-01, Section: A.
- General Note
- Advisor: Tsoukalas, Lefteri H.;Kim, Seungjin;Lopez-De-Bertodano, Martin A.;Heifetz, Alexander;Alamaniotis, Miltiadis.
- Dissertation Note
- Thesis (Ph.D.)--Purdue University, 2022.
- Restrictions on Access Note
- This item must not be sold to any third party vendors.
- Summary, Etc.
- 요약Nuclear engineering hosts diverse domains including, but not limited to, power plant automation, human-machine interfacing, detection and identification of special nuclear materials, modeling of reactor kinetics and dynamics that most frequently are described by systems of differential equations (DEs), either ordinary (ODEs) or partial ones (PDEs). In this work we study multiple problems related to safety and Special Nuclear Material detection, and numerical solutions for partial differential equations using neural networks. More specifically, this work is divided in six chapters. Chapter 1 is the introduction, in Chapter 2 we discuss the development of a gamma-ray radionuclide library for the characterization of gamma-spectra.In Chapter 3, we present a new approach, the "Variance Counterbalancing", for stochastic large-scale learning.In Chapter 4, we introduce a systematic approach for constructing proper trial solutions to partial differential equations (PDEs) of up to second order, using neural forms that satisfy prescribed initial, boundary and interface conditions.Chapter 5 is about an alternative, less imposing development of neural-form trial solutions for PDEs, inside rectangular and non-rectangular convex boundaries.Chapter 6 presents an ensemble method that avoids the multicollinearity issue and provides enhanced generalization performance that could be suitable for handling "few-shots"- problems frequently appearing in nuclear engineering.
- Subject Added Entry-Topical Term
- Heat.
- Subject Added Entry-Topical Term
- Energy.
- Subject Added Entry-Topical Term
- Nuclear engineering.
- Subject Added Entry-Topical Term
- Boundary conditions.
- Subject Added Entry-Topical Term
- Radiation.
- Subject Added Entry-Topical Term
- Data compression.
- Subject Added Entry-Topical Term
- Industrial plant emissions.
- Subject Added Entry-Topical Term
- Power plants.
- Subject Added Entry-Topical Term
- Nuclear reactors.
- Subject Added Entry-Topical Term
- Partial differential equations.
- Subject Added Entry-Topical Term
- Spectrum analysis.
- Subject Added Entry-Topical Term
- Sensors.
- Subject Added Entry-Topical Term
- Neural networks.
- Subject Added Entry-Topical Term
- Gamma rays.
- Subject Added Entry-Topical Term
- Libraries.
- Subject Added Entry-Topical Term
- Atoms & subatomic particles.
- Subject Added Entry-Topical Term
- Analytical chemistry.
- Subject Added Entry-Topical Term
- Atmospheric sciences.
- Subject Added Entry-Topical Term
- Atomic physics.
- Subject Added Entry-Topical Term
- Chemistry.
- Subject Added Entry-Topical Term
- Mathematics.
- Subject Added Entry-Topical Term
- Optics.
- Subject Added Entry-Topical Term
- Physics.
- Subject Added Entry-Topical Term
- Transportation.
- Added Entry-Corporate Name
- Purdue University.
- Host Item Entry
- Dissertations Abstracts International. 85-01A.
- Host Item Entry
- Dissertation Abstract International
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:641672
