Zettelkasten Migration Plan

Zettelkasten Migration Plan for CERN Research Vault

This document outlines the systematic transformation of the CERN research vault to follow zettelkasten principles while preserving scientific rigor and research context.

Current State Assessment

• Total notes: 834 markdown files
• Structure: PARA-inspired organization with strong MOC framework
• Strengths: Rich cross-linking, good tagging, temporal organization
• Opportunities: Atomic note creation, emergent topic clusters, enhanced discoverability

Migration Timeline

Phase 1: Foundation (Week 1-2)

Create missing atomic concepts and split most complex notes

Phase 2: Clustering (Week 3-4)

Establish emergent topic clusters that transcend current hierarchy

Phase 3: Enhancement (Week 5-6)

Enhance cross-linking and update MOC structure

Detailed Migration Plan

Phase 1: Foundation - Create Atomic Notes

1.1 Split Complex Multi-Concept Notes

Hysteresis Modeling Area

• Split Foundation model for hysteresis modeling.md into:

  • Hysteresis Simulation Artifacts.md - Smoothing and artifact avoidance
  • RDP for Hysteresis Data Compression.md - Detailed RDP application results
  • Hysteresis Foundation Model Hyperparameter Tuning.md - Grid search results

• Split Transfer learning and fine-tuning.md into:

  • Keep core transfer learning strategy in original note
  • Comparison of Pre-trained vs Raw Data Trained Hysteresis Models.md - TFTMBI-44 vs TFTMBI-50 results
  • Freezing Scaler Parameters during Fine-tuning.md - Specific technique

• Split Jiles-Atherton model for hysteresis.md into:

  • Keep general overview in original note
  • Jiles-Atherton Parameters for Iron Core Magnets.md - Specific parameter tuning

Machine Development Area

• Split Dedicated MD 2024-09-04.md into:

  • MD Plan 2024-09-04.md - Goals and preparation
  • MD Execution Log 2024-09-04.md - Live session details
  • MD Analysis 2024-09-04.md - Results and conclusions

• Split Dedicated MD 2024-10-09.md into:

  • MD Prep & Goals 2024-10-09.md - Preparation tasks
  • MD Live Log 2024-10-09.md - Detailed execution log
  • MD Results & Analysis 2024-10-09.md - Post-MD findings

• Split Spontaneous MD 2024-11-29.md into:

  • Spontaneous MD Observations 2024-11-29.md - Strange beam behavior observations
  • BPM Data Analysis for 2024-11-29 MD.md - BPM-specific analysis
  • Eddy Current Decay Analysis from MD 2024-11-29.md - Decay constant fitting

Field Compensation Area

• Split Eddy current decay in the SPS main dipoles.md into:

  • Eddy Current Decay Measurement on B-Train.md - B-Train specific methods
  • Eddy Current Decay Measurement from Beam Orbit.md - Orbit-based methods
  • Comparison of Eddy Current Measurement Techniques.md - Method comparison

• Split FGCs.md into:

  • Keep general FGC overview in original note
  • Dynamic Economy in FGCs.md - Dynamic economy mode details
  • Full Economy in FGCs.md - Full economy mode details

1.2 Create Missing Atomic Concepts

Core Technical Concepts

• SPS Main Dipoles.md - Physical properties and characteristics
• SPS Main Quadrupoles.md - Higher-order magnet properties
• B-Train Measurement System.md - Measurement methodology and equipment
• LHC Software Architecture (LSA).md - Software framework explanation
• UCAP.md - Real-time control system explanation
• NXCALS Data System.md - Data acquisition and access

Neural Network Components

• Gated Residual Network (GRN).md - TFT component architecture
• Interpretable Multi-head Attention.md - Attention mechanism variant
• Teacher Forcing in Sequence Models.md - Training strategy
• Evaluation Metrics for Hysteresis Modeling.md - RMSE, SMAPE, and domain-specific metrics

Experimental Concepts

• MD1 Pre-cycle Impact on Beam.md - Consolidated findings on MD1 effects
• B-Train Measurement Quality.md - Calibration issues and solutions
• Supercycle Transition Effects.md - Impact of cycle changes on magnetic behavior

Phase 2: Clustering - Emergent Topic Clusters

2.1 Create New Topic Cluster MOCs

Cross-Area Clusters

• MOC - TFT Experiments.md - All Temporal Fusion Transformer experiments
• MOC - Hysteresis Data Generation.md - From simulation to training data
• MOC - Hybrid RNN-Attention Models.md - Combined architecture approaches
• MOC - SPS Magnet Systems.md - All magnet-related notes
• MOC - Real-time Compensation Systems.md - Operational deployment focus
• MOC - Physics-Informed Machine Learning.md - Physics-ML integration

Measurement & Validation Clusters

• MOC - Magnetic Field Measurement.md - All measurement methodologies
• MOC - Eddy Current Studies.md - Cross-area eddy current work
• MOC - Hysteresis Compensation MDs.md - MD sessions focused on compensation
• MOC - Magnetic Measurement Campaigns.md - Dipole and quadrupole campaigns

2.2 Create Topic-Specific Concept Maps

Neural Architecture Comparisons

• AttentionLSTM vs EncoderDecoderLSTM.md - Architecture comparison
• TransformerLSTM vs AttentionLSTM.md - Hybrid model comparison
• TFT vs PhyLSTM Comparison.md - Data-driven vs physics-informed

Method Comparisons

• Hysteresis Model Comparison.md - Preisach vs Jiles-Atherton vs Bouc-Wen
• Time Series Compression Methods.md - Algorithm comparison overview

Phase 3: Enhancement - Cross-Linking and Structure

3.1 Enhance Cross-Linking

Add Bidirectional Concept Links

• Add “Related Concepts” sections to all atomic notes
• Link complementary concepts (e.g., measurement ↔ modeling)
• Connect theory ↔ application notes

Create Concept Pathways

• Physics models → Neural architectures → Experiments → Deployment
• Data acquisition → Preprocessing → Training → Validation
• Theory → Simulation → Measurement → Compensation

3.2 Update MOC Structure

Enhance Existing MOCs

• Add synthesis paragraphs to MOC - Hysteresis Modeling.md
• Add synthesis paragraphs to MOC - Machine Development.md
• Create cross-references between MOCs

Create Meta-MOCs

• MOC - Research Methodology.md - Links all methodological approaches
• MOC - SPS Accelerator Physics.md - High-level physics context

3.3 Consolidate Redundant Information

Remove Duplication

• Consolidate Dynamic Economy explanations into single canonical note
• Consolidate supercycle configuration descriptions
• Create canonical parameter reference notes

Priority Implementation Order

High Priority (Complete First)

1. Create missing atomic concepts (SPS Main Dipoles, B-Train, UCAP, LSA, NXCALS)
2. Split most complex notes (Foundation model, MD session logs, Eddy current decay)
3. Create core topic cluster MOCs (TFT Experiments, Real-time Compensation)

Medium Priority (Second Phase)

1. Create comparison notes between architectures and methods
2. Enhance cross-linking with “Related Concepts” sections
3. Create emergent topic clusters that span multiple areas

Lower Priority (Polish Phase)

1. Add synthesis to existing MOCs
2. Create meta-MOCs for high-level organization
3. Process literature notes for concept extraction

Success Metrics

Atomicity: Each note should contain exactly one concept that can stand alone Connectivity: Every note should link to at least 3 related concepts Discoverability: Concepts should be findable through multiple pathways Emergence: New connections should become apparent through the linking structure

Expected Outcomes

After migration:

• Improved concept discoverability across research areas
• Better connection between theory, simulation, and application
• Enhanced ability to identify research gaps and opportunities
• Stronger foundation for thesis writing and knowledge synthesis
• More efficient navigation between related concepts

Timeline Summary

• Week 1: Foundation atomic notes and major splits
• Week 2: Complete complex note decomposition
• Week 3: Create emergent topic clusters
• Week 4: Establish new MOCs and comparison notes
• Week 5: Enhance cross-linking and pathways
• Week 6: Polish, consolidate, and validate structure

The migration maintains your current organizational strengths while adding zettelkasten’s discoverability and connection benefits to accelerate your research and thesis development.