Material Type  

 학위논문

 

0016932780

Date and Time of Latest Transaction  

20240214100544

ISBN  

9798379840877

DDC  

621

Author  

Lagari, Pola Lydia.

Title/Author  

AI and Machine Learning for SNM Detection and Solution of PDES With Interface Conditions - [electronic resource]

Publish Info  

[S.l.] : Purdue University., 2022

Publish Info  

Ann Arbor : ProQuest Dissertations & Theses, 2022

Material Info  

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.

학위논문주기  

Thesis (Ph.D.)--Purdue University, 2022.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Abstracts/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.

소장사항  

202402 2024

Control Number  

joongbu:641672