Concept Graph & Resume using Claude 3.5 Sonnet | Chat GPT4o | Llama 3:
Resume:
1.- Fundamental questions about universe's composition and building blocks
2.- Standard Model of Particle Physics structure and symmetries
3.- Higgs boson as mechanism for particle mass
4.- Large Hadron Collider design and operation principles
5.- CMS detector's multi-layered structure for particle detection
6.- Machine learning transforming traditional particle physics approaches
7.- Ultra-fast ML processing for 40 million collisions/second
8.- FPGA implementation challenges for real-time particle detection
9.- Co-design principles combining ML algorithms with hardware constraints
10.- Quantization and pruning techniques for model compression
11.- HLS4ML framework translating ML models to hardware
12.- Anomaly detection using variational autoencoders for new physics
13.- Applications of HLS4ML beyond particle physics
14.- Multimodal ML combining different detector data types
15.- Particle flow reconstruction from detector signatures
16.- Graph neural networks for particle track reconstruction
17.- Locality-sensitive hashing reducing computational complexity
18.- Performance improvements using GPU and specialized hardware
19.- Point transformer architecture for efficient particle tracking
20.- Lorentz symmetry incorporation into neural networks
21.- Discovering physical symmetries through machine learning
22.- Generative models for particle collision simulation
23.- JetNet dataset for benchmarking generative models
24.- Message-passing GANs for physics-accurate particle generation
25.- Metrics for evaluating physics-based generative models
26.- Future collider proposals post-LHC era
27.- Differentiable programming for detector optimization
28.- Foundation models potential in particle physics
29.- Self-supervised learning approaches for physics data
30.- Physics-driven data augmentation techniques
Knowledge Vault built byDavid Vivancos 2024