From 790f9a9506c37db9a2bb3eafd880b8f8947cbe3d Mon Sep 17 00:00:00 2001 From: Dane Sabo Date: Wed, 30 Oct 2024 09:04:32 -0400 Subject: [PATCH 01/11] vault backup: 2024-10-30 09:04:32 --- 1 Daily Notes/2024/10 October/2024-10-30.md | 55 +++++++++++++++++++++ 1 file changed, 55 insertions(+) create mode 100644 1 Daily Notes/2024/10 October/2024-10-30.md diff --git a/1 Daily Notes/2024/10 October/2024-10-30.md b/1 Daily Notes/2024/10 October/2024-10-30.md new file mode 100644 index 00000000..a71af587 --- /dev/null +++ b/1 Daily Notes/2024/10 October/2024-10-30.md @@ -0,0 +1,55 @@ +--- +date: 2024-10-30 +tags: +--- +# Yesterday | Tomorrow + << [[1 Daily Notes/2024/10 October/2024-10-29]] | [[1 Daily Notes/2024/10 October/2024-10-31 ]] >> +# This Week's Weekly Note +[[ Weekly Note 2024-10-23]] +# Tasks for today +## Plan 😎 +1. Capture stuff for Lauren and GSA +2. Finish NUCE assignment +3. ME2016 Miniproject +4. QE Research approach, diffusion papers. +## Due +```dataview +task +where + due <= date(this.date) + and due + and !completed + and status != "-" +sort due asc +group by file.folder +``` +## Scheduled +```dataview +task +where + scheduled + and scheduled <= date(this.date) + and !completed + and status != "-" +sort due asc +group by file.folder +``` +## Tasks in Progress +```dataview +task +where +status != "-" +and status = "/" +sort due asc +group by file.folder +``` +## Completed +```dataview +task +where + completed + and completion = date(this.date) +sort due asc +group by file.folder +``` +# Calendar Tasks \ No newline at end of file From 80813f5ab45e09eeef98b6db98bfcdcb7e3ac2ac Mon Sep 17 00:00:00 2001 From: Dane Sabo Date: Wed, 30 Oct 2024 09:10:31 -0400 Subject: [PATCH 02/11] vault backup: 2024-10-30 09:10:31 --- 1 Daily Notes/2024/10 October/2024-10-30.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/1 Daily Notes/2024/10 October/2024-10-30.md b/1 Daily Notes/2024/10 October/2024-10-30.md index a71af587..88da961d 100644 --- a/1 Daily Notes/2024/10 October/2024-10-30.md +++ b/1 Daily Notes/2024/10 October/2024-10-30.md @@ -8,7 +8,7 @@ tags: [[ Weekly Note 2024-10-23]] # Tasks for today ## Plan 😎 -1. Capture stuff for Lauren and GSA +1. Capture stuff for Lauren and GSA Done! 2. Finish NUCE assignment 3. ME2016 Miniproject 4. QE Research approach, diffusion papers. From 9b80a0a1173c6486311bab208de4a5b239307aae Mon Sep 17 00:00:00 2001 From: Dane Sabo Date: Wed, 30 Oct 2024 10:59:20 -0400 Subject: [PATCH 03/11] vault backup: 2024-10-30 10:59:20 --- 201 Metadata/My Library.bib | 275 ++++++++++++++++++++++++++++++++++++ 1 file changed, 275 insertions(+) diff --git a/201 Metadata/My Library.bib b/201 Metadata/My Library.bib index c33e90f0..1748a87c 100644 --- a/201 Metadata/My Library.bib +++ b/201 Metadata/My Library.bib @@ -3527,6 +3527,246 @@ Artificial Intelligence Program.pdf} file = {/home/danesabo/Zotero/storage/H2IWZM6I/Ellison et al. - Extending AADL for Security Design Assurance of Cy.pdf} } +@online{ENGR2100Module, + title = {{{ENGR}} 2100 {{Module}} 7.1 - {{Point Kinetics}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486309?module_item_id=5008204}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/BZCBC5EA/ENGR 2100 Module 7.1 - Point Kinetics.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf;/home/danesabo/Zotero/storage/VYKI2KYI/17486309.html} +} + +@online{ENGR2100Modulea, + title = {{{ENGR}} 2100 {{Module}} 5 {{Review}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486295?module_item_id=5008181}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/S3CN9V7B/ENGR 2100 Module 5 Review.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Moduleaa, + title = {{{ENGR}} 2100 {{Module}} 2.1 - {{Binding Energy}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486231?module_item_id=5008128}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/ZII4DNAP/ENGR 2100 Module 2.1 - Binding Energy.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Moduleab, + title = {{{ENGR}} 2100 {{Module}} 3 {{Review}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486237?module_item_id=5008150}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/4EY8DSX6/ENGR 2100 Module 3 Review.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Moduleac, + title = {{{ENGR}} 2100 {{Module}} 3.5 - {{Radiation Shielding}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486228?module_item_id=5008149}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/MTUUIMIW/ENGR 2100 Module 3.5 - Radiation Shielding.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Modulead, + title = {{{ENGR}} 2100 {{Module}} 3.4 - {{Estimating Radiation Dose Rates}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486246?module_item_id=5008148}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/BK9ESLNN/ENGR 2100 Module 3.4 - Estimating Radiation Dose Rates.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCL.pdf} +} + +@online{ENGR2100Moduleae, + title = {{{ENGR}} 2100 {{Module}} 3.3 - {{Radiation Protection Standards}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486272?module_item_id=5008147}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/PVUK34DK/ENGR 2100 Module 3.3 - Radiation Protection Standards.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLE.pdf} +} + +@online{ENGR2100Moduleaf, + title = {{{ENGR}} 2100 {{Module}} 3.2 - {{Radiation Damage}} in {{Biological Systems}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486242?module_item_id=5008146}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/GI4SIPXM/ENGR 2100 Module 3.2 - Radiation Damage in Biological Systems.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTA.pdf} +} + +@online{ENGR2100Moduleag, + title = {{{ENGR}} 2100 {{Module}} 2.3 - {{Nuclear Reactions}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486303?module_item_id=5008130}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/9DQW3AW8/ENGR 2100 Module 2.3 - Nuclear Reactions.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Moduleah, + title = {{{ENGR}} 2100 {{Module}} 5.1 - {{Reactor Overview}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486193?module_item_id=5008177}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/3U2DTM8N/ENGR 2100 Module 5.1 - Reactor Overview.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Moduleb, + title = {{{ENGR}} 2100 {{Module}} 5.4 - {{Criticality Control}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486218?module_item_id=5008180}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/TFD7HEZ5/ENGR 2100 Module 5.4 - Criticality Control.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Modulec, + title = {{{ENGR}} 2100 {{Module}} 5.3 - {{PWR}} and {{BWR Cores}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486253?module_item_id=5008179}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/N4IJCJT8/ENGR 2100 Module 5.3 - PWR and BWR Cores.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Moduled, + title = {{{ENGR}} 2100 {{Module}} 6 {{Review}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486297?module_item_id=5008195}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/9T6YSYIW/ENGR 2100 Module 6 Review.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Modulee, + title = {{{ENGR}} 2100 {{Module}} 6.4 - 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Diffusion Theory.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Moduleh, + title = {{{ENGR}} 2100 {{Module}} 6.1 - {{Neutron Balance}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486280?module_item_id=5008191}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/FWXDMAAY/ENGR 2100 Module 6.1 - Neutron Balance.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Modulei, + title = {{{ENGR}} 2100 {{Module}} 7 {{Review}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486250?module_item_id=5008206}, + urldate = {2024-10-29} +} + +@online{ENGR2100Modulej, + title = {{{ENGR}} 2100 {{Module}} 7.2 - {{Reactivity Coefficients}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486310?module_item_id=5008205}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/UUQC82A7/ENGR 2100 Module 7.2 - Reactivity Coefficients.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Modulek, + title = {{{ENGR}} 2100 {{Module}} 4 {{Review}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486268?module_item_id=5008168}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/LKE7M3DS/ENGR 2100 Module 4 Review.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Modulel, + title = {{{ENGR}} 2100 {{Module}} 4.4 - {{Nuclear Materials}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486263?module_item_id=5008167}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/45PXPH3Y/ENGR 2100 Module 4.4 - Nuclear Materials.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Modulem, + title = {{{ENGR}} 2100 {{Module}} 4.3 - {{Reactor Types}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486252?module_item_id=5008166}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/HRN2L59G/ENGR 2100 Module 4.3 - Reactor Types.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Modulen, + title = {{{ENGR}} 2100 {{Module}} 4.2 - {{Fuel Cycle Back End}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486274?module_item_id=5008165}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/F7CZAE7N/ENGR 2100 Module 4.2 - Fuel Cycle Back End.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Moduleo, + title = {{{ENGR}} 2100 {{Module}} 4.1 - {{Intro}} \& {{Fuel Cycle Front End}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486251?module_item_id=5008164}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/F5283757/ENGR 2100 Module 4.1 - Intro & Fuel Cycle Front End.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR.pdf} +} + +@online{ENGR2100Modulep, + title = {{{ENGR}} 2100 {{Module}} 5.2 - {{Neutron Multiplication}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486255?module_item_id=5008178}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/SRXCXH5Y/ENGR 2100 Module 5.2 - Neutron Multiplication.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Moduleq, + title = {{{ENGR}} 2100 {{Module}} 1 {{Review}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486233?module_item_id=5008117}, + urldate = {2024-10-29} +} + +@online{ENGR2100Moduler, + title = {{{ENGR}} 2100 {{Module}} 1.3 - {{Decay Radiation}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486195?module_item_id=5008116}, + urldate = {2024-10-29} +} + +@online{ENGR2100Modules, + title = {{{ENGR}} 2100 {{Module}} 1.2 - {{Nuclear Decay}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486243?module_item_id=5008115}, + urldate = {2024-10-29} +} + +@online{ENGR2100Modulet, + title = {{{ENGR}} 2100 {{Module}} 1.1 - {{Fundamental Particles}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486249?module_item_id=5008114}, + urldate = {2024-10-29} +} + +@online{ENGR2100Moduleu, + title = {{{ENGR}} 2100 {{Module}} 2 {{Review}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486192?module_item_id=5008134}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/FDD69ZAC/ENGR 2100 Module 2 Review.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Modulev, + title = {{{ENGR}} 2100 {{Module}} 3.1 - {{Radiation Terminology}} and {{Units}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486209?module_item_id=5008145}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/8S3ZW8AQ/ENGR 2100 Module 3.1 - Radiation Terminology and Units.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCL.pdf} +} + +@online{ENGR2100Modulew, + title = {{{ENGR}} 2100 {{Module}} 2.6 - {{Neutron Reactions}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486247?module_item_id=5008133}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/K84ZCZ7D/ENGR 2100 Module 2.6 - Neutron Reactions.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Modulex, + title = {{{ENGR}} 2100 {{Module}} 2.5 - {{Atom Density}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486207?module_item_id=5008132}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/AYI48LNJ/ENGR 2100 Module 2.5 - Atom Density.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Moduley, + title = {{{ENGR}} 2100 {{Module}} 2.4 - {{Nuclear Fission}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486200?module_item_id=5008131}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/NF6FA8XG/ENGR 2100 Module 2.4 - Nuclear Fission.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + +@online{ENGR2100Modulez, + title = {{{ENGR}} 2100 {{Module}} 2.2 - {{Radiation Interactions}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486199?module_item_id=5008129}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/AGZDPXWJ/ENGR 2100 Module 2.2 - Radiation Interactions.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf} +} + @book{EnhancingEffectivenessTeam2015, title = {Enhancing the {{Effectiveness}} of {{Team Science}}}, date = {2015-07-15}, @@ -7732,6 +7972,41 @@ Insights from the Social Sciences.pdf} urldate = {2024-07-10} } +@online{Module81Fission, + title = {Module 8.1 - {{Fission Heat Generation-1}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486316?module_item_id=5008211}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/DVCE48RB/Module 8.1 - Fission Heat Generation-1.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf;/home/danesabo/Zotero/storage/PUILTK75/17486316.html} +} + +@online{Module82Decay, + title = {Module 8.2 - {{Decay Heat}}, {{Plant Parameters}}, {{Design-1}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486248?module_item_id=5008212}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/WCPGR2IY/Module 8.2 - Decay Heat, Plant Parameters, Design-1.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR.pdf} +} + +@online{Module83Heat, + title = {Module 8.3 - {{Heat Conduction-1}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486217?module_item_id=5008213}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/EAWWPR9P/Module 8.3 - Heat Conduction-1.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf;/home/danesabo/Zotero/storage/RQ5ICY3R/17486217.html} +} + +@online{Module8Class, + title = {Module 8 {{Class Notes-1}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486215?module_item_id=5008215}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/LQN7X96B/Module 8 Class Notes-1.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf;/home/danesabo/Zotero/storage/EU6QMXAZ/17486215.html} +} + +@online{Module8Review1pdf, + title = {Module 8 {{Review-1}}.Pdf: 2251 {{NUCE}} 2100 {{SEC1250 FUNDAMENTALS NUCLEAR ENGR}}}, + url = {https://canvas.pitt.edu/courses/280885/files/17486300?module_item_id=5008214}, + urldate = {2024-10-29}, + file = {/home/danesabo/Zotero/storage/JMCTU5LC/Module 8 Review-1.pdf 2251 NUCE 2100 SEC1250 FUNDAMENTALS NUCLEAR ENGR.pdf;/home/danesabo/Zotero/storage/TRAWYPDA/17486300.html} +} + @inproceedings{mohanS3ASecureSystem2013, title = {{{S3A}}: Secure System Simplex Architecture for Enhanced Security and Robustness of Cyber-Physical Systems}, shorttitle = {{{S3A}}}, From 0542f890b52016c21af0630cd16e76cf4d22fed0 Mon Sep 17 00:00:00 2001 From: Dane Sabo Date: Wed, 30 Oct 2024 11:13:21 -0400 Subject: [PATCH 04/11] vault backup: 2024-10-30 11:13:21 --- .obsidian/graph.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.obsidian/graph.json b/.obsidian/graph.json index acb1cad5..cce98473 100755 --- a/.obsidian/graph.json +++ b/.obsidian/graph.json @@ -67,6 +67,6 @@ "repelStrength": 12.5, "linkStrength": 1, "linkDistance": 140, - "scale": 0.3869620710498517, + "scale": 0.1719831426888229, "close": true } \ No newline at end of file From ac4ba741df5cde308f1f2c6bb4dd4bf2fc68549f Mon Sep 17 00:00:00 2001 From: Dane Sabo Date: Wed, 30 Oct 2024 13:30:08 -0400 Subject: [PATCH 05/11] vault backup: 2024-10-30 13:30:08 --- 201 Metadata/My Library.bib | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/201 Metadata/My Library.bib b/201 Metadata/My Library.bib index 1748a87c..d9af3ab0 100644 --- a/201 Metadata/My Library.bib +++ b/201 Metadata/My Library.bib @@ -649,6 +649,17 @@ Opportunities and Challenges toward Responsible AI.pdf} } +@video{artemkirsanovKeyEquationProbability2024, + entrysubtype = {video}, + title = {The {{Key Equation Behind Probability}}}, + editor = {{Artem Kirsanov}}, + editortype = {director}, + date = {2024-08-22}, + url = {https://www.youtube.com/watch?v=KHVR587oW8I}, + urldate = {2024-10-30}, + abstract = {Get 4 months extra on a 2 year plan here: https://nordvpn.com/artemkirsanov. It’s risk free with Nord’s 30 day money-back guarantee! Socials: X/Twitter: https://x.com/ArtemKRSV Patreon: ~~/~artemkirsanov~~ My name is Artem, I'm a graduate student at NYU Center for Neural Science and researcher at Flatiron Institute (Center for Computational Neuroscience). In this video, we explore the fundamental concepts that underlie probability theory and its applications in neuroscience and machine learning. We begin with the intuitive idea of surprise and its relation to probability, using real-world examples to illustrate these concepts. From there, we move into more advanced topics: 1) Entropy – measuring the average surprise in a probability distribution. 2) Cross-entropy and the loss of information when approximating one distribution with another. 3) Kullback-Leibler (KL) divergence and its role in quantifying the difference between two probability distributions. OUTLINE: 00:00 Introduction 02:00 Sponsor: NordVPN 04:07 What is probability (Bayesian vs Frequentist) 06:42 Probability Distributions 10:17 Entropy as average surprisal 13:53 Cross-Entropy and Internal models 19:20 Kullback–Leibler (KL) divergence 20:46 Objective functions and Cross-Entropy minimization 24:22 Conclusion \& Outro CREDITS: Special thanks to Crimson Ghoul for providing English subtitles! Icons by https://www.freepik.com/} +} + @book{arthoFormalTechniquesSafetyCritical2015, title = {Formal {{Techniques}} for {{Safety-Critical Systems}}}, author = {Artho, Cyrille and Ölveczky, Peter Csaba}, From 6fbfa029dd24fe64fbb005ef7fcd3f6c473813c8 Mon Sep 17 00:00:00 2001 From: Dane Sabo Date: Wed, 30 Oct 2024 13:47:12 -0400 Subject: [PATCH 06/11] vault backup: 2024-10-30 13:47:12 --- 201 Metadata/My Library.bib | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/201 Metadata/My Library.bib b/201 Metadata/My Library.bib index d9af3ab0..5ebba422 100644 --- a/201 Metadata/My Library.bib +++ b/201 Metadata/My Library.bib @@ -7521,6 +7521,17 @@ for defect classification of TFT–LCD panels.pdf} urldate = {2024-01-28} } +@video{matlabInfinityMuSynthesis2020, + entrysubtype = {video}, + title = {H {{Infinity}} and {{Mu Synthesis}} | {{Robust Control}}, {{Part}} 5}, + editor = {{MATLAB}}, + editortype = {director}, + date = {2020-05-19}, + url = {https://www.youtube.com/watch?v=kRt7H0k8A4k}, + urldate = {2024-10-30}, + abstract = {This video walks through a controller design for an active suspension system. Actually, we design two controllers. For the first, we use H infinity synthesis to design a controller for a nominal plant model that will guarantee performance but not necessarily be robust to variation in the system. Then we build an uncertain model like we did in the last video and design a robust controller using mu synthesis. Watch the first videos in this series: Robust Control, Part 1: What Is Robust Control? - ~~~•~What~Is~Robust~Control?~|~Robust~Cont...~~ Robust Control, Part 2: Understanding Disk Margin - ~~~•~Understanding~Disk~Margin~|~Robust~Co...~~ Robust Control, Part 3: Disk Margins for MIMO Systems - ~~~•~Disk~Margins~for~MIMO~Systems~|~Robus...~~ Robust Control, Part 4: Working with Parameter Uncertainty - ~~~•~Working~with~Parameter~Uncertainty~|~...~~ Check out these other references: Robust Control of an Active Suspension: https://bit.ly/3bt8VCE -------------------------------------------------------------------------------------------------------- Get a free product trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See what's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2020 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.} +} + @article{mattosLatentAutoregressiveGaussian2016, title = {Latent {{Autoregressive Gaussian Processes Models}} for {{Robust System Identification}}}, author = {Mattos, César Lincoln C. and Damianou, Andreas and Barreto, Guilherme A. and Lawrence, Neil D.}, From ff668bb7eaf3fd8a819f46fe6075f9e2700d4bf0 Mon Sep 17 00:00:00 2001 From: Dane Sabo Date: Wed, 30 Oct 2024 14:46:27 -0400 Subject: [PATCH 07/11] vault backup: 2024-10-30 14:46:27 --- 201 Metadata/My Library.bib | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/201 Metadata/My Library.bib b/201 Metadata/My Library.bib index 5ebba422..77bc852c 100644 --- a/201 Metadata/My Library.bib +++ b/201 Metadata/My Library.bib @@ -5545,6 +5545,13 @@ Regulatory Premises.pdf} urldate = {2024-08-08} } +@online{HttpsVbnaaudkWs, + title = {{{https://vbn.aau.dk/ws/portalfiles/portal/140575/fulltext}}}, + url = {https://vbn.aau.dk/ws/portalfiles/portal/140575/fulltext}, + urldate = {2024-10-30}, + file = {/home/danesabo/Zotero/storage/3LAD5CQX/fulltext.pdf} +} + @online{HttpsWwwWhitehouse, title = {{{https://www.whitehouse.gov/wp-content/uploads/2024/02/Final-ONCD-Technical-Report.pdf}}}, url = {https://www.whitehouse.gov/wp-content/uploads/2024/02/Final-ONCD-Technical-Report.pdf}, @@ -11867,6 +11874,17 @@ Subject\_term: Careers, Politics, Policy}, annotation = {Page Version ID: 1191589904} } +@article{toffner-clausenMuSynthesisMuSynthesis1995, + title = {Mu-{{Synthesis}}: {{Mu-Synthesis}}}, + shorttitle = {Mu-{{Synthesis}}}, + author = {Tøffner-Clausen, S. and Andersen, Palle}, + date = {1995}, + journaltitle = {Recent Results in Robust and Adaptive Control, EURACO Workshop Florence 11-14 September 1995}, + pages = {269--303}, + abstract = {This paper provides an introduction to mu-synthesis.}, + file = {/home/danesabo/Zotero/storage/MCTBWR4Y/fulltext.pdf} +} + @article{tomlinComputationalTechniquesVerification2003, title = {Computational Techniques for the Verification of Hybrid Systems}, author = {Tomlin, C.J. and Mitchell, I. and Bayen, A.M. and Oishi, M.}, From b233f175f1fd61d1c63d5bdcdfb7e03b76b9f153 Mon Sep 17 00:00:00 2001 From: Dane Sabo Date: Wed, 30 Oct 2024 15:07:24 -0400 Subject: [PATCH 08/11] vault backup: 2024-10-30 15:07:24 --- 201 Metadata/My Library.bib | 34 +++++++++++++++++++ .../2 Writing/2. QE State of the Art.md | 1 - .../2 Writing/3. QE Research Approach.md | 3 ++ 3 files changed, 37 insertions(+), 1 deletion(-) diff --git a/201 Metadata/My Library.bib b/201 Metadata/My Library.bib index 77bc852c..b6313773 100644 --- a/201 Metadata/My Library.bib +++ b/201 Metadata/My Library.bib @@ -12622,6 +12622,22 @@ Subject\_term: Careers, Politics, Policy}, pages = {681--688} } +@article{wenFeedbackLinearizationControl2024, + title = {Feedback Linearization Control for Uncertain Nonlinear Systems via Generative Adversarial Networks}, + author = {Wen, Nuan and Liu, Zhenghua and Wang, Weihong and Wang, Shaoping}, + date = {2024-03-01}, + journaltitle = {ISA Transactions}, + shortjournal = {ISA Transactions}, + volume = {146}, + pages = {555--566}, + issn = {0019-0578}, + doi = {10.1016/j.isatra.2023.12.033}, + url = {https://www.sciencedirect.com/science/article/pii/S001905782300592X}, + urldate = {2024-10-30}, + abstract = {This article presents a novel approach to leverage generative adversarial networks(GANs) techniques to learn a feedback linearization controller(FLC) for a class of uncertain nonlinear systems. By estimating uncertainty through the adversarial process, where ground truth samples are exclusively obtained from a predefined integral model, the feedback linearization controller, learned through a minimax two-player optimization framework, enhances the reference tracking performance of the input-output uncertain nonlinear system. Furthermore, we provide theoretical guarantee of convergence and stability, demonstrating the safe recovery of robust FLC. We also address the common challenge of mode collapse in GANs training through the strict convexity of our synthesized generator structure and an enhanced adversarial loss. Comprehensive simulations and practical experiments are conducted to underscore the superiority and efficacy of our proposed approach.}, + keywords = {Convex optimization,Feedback linearization,Generative adversarial networks,Nonlinear systems} +} + @online{wengAutoencoderBetaVAE2018, title = {From {{Autoencoder}} to {{Beta-VAE}}}, author = {Weng, Lilian}, @@ -13110,6 +13126,24 @@ Subject\_term: Careers, Politics, Policy}, file = {/home/danesabo/Zotero/storage/7RV26D7X/Zhao et al. - 2021 - Neural Lyapunov Control for Power System Transient.pdf} } +@article{zhaoRobustVoltageControl2020, + title = {Robust {{Voltage Control Considering Uncertainties}} of {{Renewable Energies}} and {{Loads}} via {{Improved Generative Adversarial Network}}}, + author = {Zhao, Qianyu and Liao, Wenlong and Wang, Shouxiang and Pillai, Jayakrishnan Radhakrishna}, + date = {2020-11}, + journaltitle = {Journal of Modern Power Systems and Clean Energy}, + volume = {8}, + number = {6}, + pages = {1104--1114}, + issn = {2196-5420}, + doi = {10.35833/MPCE.2020.000210}, + url = {https://ieeexplore.ieee.org/abstract/document/9275598}, + urldate = {2024-10-30}, + abstract = {The fluctuation of output power of renewable energies and loads brings challenges to the scheduling and operation of the distribution network. In this paper, a robust voltage control model is proposed to cope with the uncertainties of renewable energies and loads based on an improved generative adversarial network (IGAN). Firstly, both real and predicted data are used to train the IGAN consisting of a discriminator and a generator. The noises sampled from the Gaussian distribution are fed to the generator to generate a large number of scenarios that are utilized for robust voltage control after scenario reduction. Then, a new improved wolf pack algorithm (IWPA) is presented to solve the formulated robust voltage control model, since the accuracy of the solutions obtained by traditional methods is limited. The simulation results show that the IGAN can accurately capture the probability distribution characteristics and dynamic nonlinear characteristics of renewable energies and loads, which makes the scenarios generated by IGAN more suitable for robust voltage control than those generated by traditional methods. Furthermore, IWPA has a better performance than traditional methods in terms of convergence speed, accuracy, and stability for robust voltage control.}, + eventtitle = {Journal of {{Modern Power Systems}} and {{Clean Energy}}}, + keywords = {Distribution networks,Gallium nitride,generative adversarial network,Generative adversarial networks,Load modeling,Power system stability,Robust voltage control,uncertainty,Uncertainty,Voltage control,wolf pack algorithm}, + file = {/home/danesabo/Zotero/storage/2MACG5H9/Zhao et al. - 2020 - Robust Voltage Control Considering Uncertainties of Renewable Energies and Loads via Improved Genera.pdf} +} + @article{zhaoStabilityL2gainControl2008, title = {On Stability, {{L2-gain}} and {{H}}∞ Control for Switched Systems}, author = {Zhao, Jun and Hill, David J.}, diff --git a/4 Qualifying Exam/2 Writing/2. QE State of the Art.md b/4 Qualifying Exam/2 Writing/2. QE State of the Art.md index 511bfc9f..20ac27aa 100644 --- a/4 Qualifying Exam/2 Writing/2. QE State of the Art.md +++ b/4 Qualifying Exam/2 Writing/2. QE State of the Art.md @@ -45,4 +45,3 @@ This is useful for us. If we can find an uncertainty transfer function $W_2$ tha $\Delta$ is almost always considered a free variable transfer function. Since $||\Delta||_\infty < 1 \text{ } \forall \omega$, $\Delta$ will not decrease the minimum robustness margin. This is fine for developing a controller, but when it comes to actually verifying robustness of a controller implementation, $\Delta$ cannot be a variable. To create a plant to simulate a perturbed plant, $\Delta$ must have an expression. **Limitation**: *There is no current method for creating random examples of $\Delta$.* Because of this, it is not currently possible to test implementations of controllers against unstructured perturbations. - diff --git a/4 Qualifying Exam/2 Writing/3. QE Research Approach.md b/4 Qualifying Exam/2 Writing/3. QE Research Approach.md index 10d10aa3..c3481bc0 100644 --- a/4 Qualifying Exam/2 Writing/3. QE Research Approach.md +++ b/4 Qualifying Exam/2 Writing/3. QE Research Approach.md @@ -39,5 +39,8 @@ Something to justify, why diffusion model as opposed to other generative AI 16. Well we train a neural network as a denoiser. 17. Because the diffusion model forward steps are small and gaussian, we can know the reverse step is also a gaussian distribution. 18. So for our neural network, what we're trying to learn is the mean and standard deviation of the reverse steps for a given timestep. +19. What does this mean for ## Writin some stuff + +The purpose of this proposal is to suggest that using a generative network to create unstructured perturbations can be a viable way to advance the state of the art. But to do this, the current state of diffusion models and their place must be introduced. The generative diffusion model is a recent breakthrough in generative models. Diffusion models \ No newline at end of file From 42c8ed0c229e8cfb87b7dfaf3c361332816904a1 Mon Sep 17 00:00:00 2001 From: Dane Sabo Date: Wed, 30 Oct 2024 15:17:08 -0400 Subject: [PATCH 09/11] vault backup: 2024-10-30 15:17:08 --- 201 Metadata/My Library.bib | 9 +++++++++ 4 Qualifying Exam/2 Writing/3. QE Research Approach.md | 2 +- 2 files changed, 10 insertions(+), 1 deletion(-) diff --git a/201 Metadata/My Library.bib b/201 Metadata/My Library.bib index b6313773..e7bb15a7 100644 --- a/201 Metadata/My Library.bib +++ b/201 Metadata/My Library.bib @@ -11227,6 +11227,15 @@ Subject\_term: Careers, Politics, Policy}, isbn = {1-4612-0577-8} } +@online{SoraCreatingVideo, + title = {Sora: {{Creating}} Video from Text}, + shorttitle = {Sora}, + url = {https://openai.com/index/sora/}, + urldate = {2024-10-30}, + langid = {american}, + file = {/home/danesabo/Zotero/storage/YUQHRZUS/sora.html} +} + @misc{sorensenLecturesCurryHowardIsomorphism, title = {Lectures on the {{Curry-Howard Isomorphism}}}, author = {Sorensen, Morten Heine B. and Urzyczyn, Pawel}, diff --git a/4 Qualifying Exam/2 Writing/3. QE Research Approach.md b/4 Qualifying Exam/2 Writing/3. QE Research Approach.md index c3481bc0..5dc3b074 100644 --- a/4 Qualifying Exam/2 Writing/3. QE Research Approach.md +++ b/4 Qualifying Exam/2 Writing/3. QE Research Approach.md @@ -43,4 +43,4 @@ Something to justify, why diffusion model as opposed to other generative AI ## Writin some stuff -The purpose of this proposal is to suggest that using a generative network to create unstructured perturbations can be a viable way to advance the state of the art. But to do this, the current state of diffusion models and their place must be introduced. The generative diffusion model is a recent breakthrough in generative models. Diffusion models \ No newline at end of file +The purpose of this proposal is to suggest that using a generative network to create unstructured perturbations can be a viable way to advance the state of the art. But to do this, the current state of diffusion models and their place must be introduced. The generative diffusion model is a recent breakthrough in generative models [@sohl-dicksteinDeepUnsupervisedLearning2015]. Diffusion generative models are the state of the art for image and video generation, and have demonstrated promise for audio generation and noise removal [@kongDiffWaveVersatileDiffusion2020] [@SoraCreatingVideo]. Diffusion models do this through a forward noise-inducing process, and a backwards \ No newline at end of file From 75c96d252b2d5e2e1fb765418a19ee8b778b3ecb Mon Sep 17 00:00:00 2001 From: Dane Sabo Date: Wed, 30 Oct 2024 15:21:21 -0400 Subject: [PATCH 10/11] vault backup: 2024-10-30 15:21:21 --- 4 Qualifying Exam/2 Writing/3. QE Research Approach.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/4 Qualifying Exam/2 Writing/3. QE Research Approach.md b/4 Qualifying Exam/2 Writing/3. QE Research Approach.md index 5dc3b074..bc04587c 100644 --- a/4 Qualifying Exam/2 Writing/3. QE Research Approach.md +++ b/4 Qualifying Exam/2 Writing/3. QE Research Approach.md @@ -43,4 +43,4 @@ Something to justify, why diffusion model as opposed to other generative AI ## Writin some stuff -The purpose of this proposal is to suggest that using a generative network to create unstructured perturbations can be a viable way to advance the state of the art. But to do this, the current state of diffusion models and their place must be introduced. The generative diffusion model is a recent breakthrough in generative models [@sohl-dicksteinDeepUnsupervisedLearning2015]. Diffusion generative models are the state of the art for image and video generation, and have demonstrated promise for audio generation and noise removal [@kongDiffWaveVersatileDiffusion2020] [@SoraCreatingVideo]. Diffusion models do this through a forward noise-inducing process, and a backwards \ No newline at end of file +The purpose of this proposal is to suggest that using a generative network to create unstructured perturbations can be a viable way to advance the state of the art. But to do this, the current state of diffusion models and their place must be introduced. The generative diffusion model is a recent breakthrough in generative models [@sohl-dicksteinDeepUnsupervisedLearning2015]. Diffusion generative models are the state of the art for image and video generation, and have demonstrated promise for audio generation and noise removal [@kongDiffWaveVersatileDiffusion2020] [@SoraCreatingVideo]. A diffusion generative model, AlphaFold 3, won the Nobel Prize in Chemistry [@AlphaFold3Predicts2024] Diffusion models do this through a forward noise-inducing process, and a learned backwards noise-removing process. \ No newline at end of file From 34347da6caf4fa4393c4359e91cadf762d8f87c8 Mon Sep 17 00:00:00 2001 From: Dane Sabo Date: Wed, 30 Oct 2024 16:26:59 -0400 Subject: [PATCH 11/11] vault backup: 2024-10-30 16:26:59 --- .../2 Writing/3. QE Research Approach.md | 23 +++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/4 Qualifying Exam/2 Writing/3. QE Research Approach.md b/4 Qualifying Exam/2 Writing/3. QE Research Approach.md index bc04587c..64728d00 100644 --- a/4 Qualifying Exam/2 Writing/3. QE Research Approach.md +++ b/4 Qualifying Exam/2 Writing/3. QE Research Approach.md @@ -39,8 +39,27 @@ Something to justify, why diffusion model as opposed to other generative AI 16. Well we train a neural network as a denoiser. 17. Because the diffusion model forward steps are small and gaussian, we can know the reverse step is also a gaussian distribution. 18. So for our neural network, what we're trying to learn is the mean and standard deviation of the reverse steps for a given timestep. -19. What does this mean for +19. What does this mean for perturbed plants? +20. Well, we can use a diffusion model to generate new perturbed plants that are similar to the original plant +21. How? We train a diffusion model on a structured set. +22. We need to pick a uncertainty function W_2 +23. Do this using the usual means. What is the worst we expect to handle? +24. We create a structured set by picking random scalar gains for $\Delta$ +25. This is our training data. We train the diffusion model to learn to this data +26. Then, we take the nominal plant, and take some amount of steps forward. +27. We heuristically go a certain amount forward to introduce a certain amount of noise +28. then we go backwards. The diffusion model will try to remove the noise, but not knowing the orignial nominal plant will introduce a perturbation. +29. **This is outcome number 3 and number 2** +30. We can use this frequency response data to find the 'worst case' distance to the critical point -1. We plot this location in the complex plane. +31. Why do we care about the complex plane? +32. This is where the nyquist robust stability and performance criterion live. +33. Our 'valid' perturbations live inside this circle of radius -1 +34. We repeat this several times, and only include examples in our set of unstructured perturbations that are within or robustness circle +35. **This is outcome number 1** +36. We generate enough examples to populate this circle until we're comfortable. ## Writin some stuff -The purpose of this proposal is to suggest that using a generative network to create unstructured perturbations can be a viable way to advance the state of the art. But to do this, the current state of diffusion models and their place must be introduced. The generative diffusion model is a recent breakthrough in generative models [@sohl-dicksteinDeepUnsupervisedLearning2015]. Diffusion generative models are the state of the art for image and video generation, and have demonstrated promise for audio generation and noise removal [@kongDiffWaveVersatileDiffusion2020] [@SoraCreatingVideo]. A diffusion generative model, AlphaFold 3, won the Nobel Prize in Chemistry [@AlphaFold3Predicts2024] Diffusion models do this through a forward noise-inducing process, and a learned backwards noise-removing process. \ No newline at end of file +The purpose of this proposal is to suggest that using a generative network to create unstructured perturbations can be a viable way to advance the state of the art. But to do this, the current state of diffusion models and their place must be introduced. The generative diffusion model is a recent breakthrough in generative models [@sohl-dicksteinDeepUnsupervisedLearning2015]. Diffusion generative models are the state of the art for image and video generation, and have demonstrated promise for audio generation and noise removal [@kongDiffWaveVersatileDiffusion2020] [@SoraCreatingVideo]. A diffusion generative model, AlphaFold 3, won the Nobel Prize in Chemistry [@AlphaFold3Predicts2024] Diffusion models do this through a forward noise-inducing process, and a learned backwards noise-removing process. + +The forward diffusion process works by introducing small amounts of noise into \ No newline at end of file