| AF-165 | Machine Learning Meets the Challenges of HEP Research and Development | |
| CF-251 | An Intelligent Platform for Theoretical Understandings | |
| CommF-047 | Transformative Technology for FLASH Radiation Therapy | |
| CommF-054 | computational | |
| CompF-009 | Pre-Learning a Geometry Using Machine Learning to | |
| CompF-010 | High Performance Computers as a solution for Machine Learning training | |
| CompF-015 | Computing, Software, and Data Analysis at Belle II | |
| CompF-016 | Energy reconstruction technique for very high energy | |
| CompF-019 | IceCube and IceCube-Gen2 Machine Learning | |
| CompF-024 | Novel Method of Jet Clustering for Future Energy | |
| CompF-025 | Symmetry Group Equivariant Architectures for Particle Physics | |
| CompF-037 | Page 1 of 2 | |
| CompF-045 | Algorithmic Advances for Processing Data from Cosmological Surveys | |
| CompF-046 | Emerging Computational Techniques for Jet Physics | |
| CompF-046 | Differentiable Programming in High-Energy Physics | |
| CompF-051 | Software and Statistics for Discovery in Cosmic | |
| CompF-061 | Machine learning for sampling in lattice quantum field theory | |
| CompF-066 | Topic Areas CompF03, CompF07 | |
| CompF-068 | Topic Areas CompF03, CompF07 | |
| CompF-075 | Machine learning and surrogate models for simulation-based | |
| CompF-078 | Machine Learning and Neural Networks for Field Theory | |
| CompF-084 | The Future of Machine Learning in Rare Event Searches | |
| CompF-086 | Building Emulators for the Cosmic Frontier | |
| CompF-096 | Machine Learning for Lattice QCD Simulations | |
| CompF-103 | Cycle and symbiosis AI and Cosmology | |
| CompF-106 | Towards the Scalable Quantum Machine Learning | |
| CompF-108 | Application of Machine Learning to Particle Accelerator | |
| CompF-109 | Scientific AI Approaches in Computational Cosmology | |
| CompF-112 | Machine Learning Techniques and Software for Neutrino Physics | |
| CompF-114 | Digging for elusive BSM with simplified models | |
| CompF-118 | Graph Data Structures and Graph Neural | |
| CompF-121 | Matrix Element Method in the Machine Learning Era | |
| CompF-123 | Supporting Research at the Intersection of Physics and Machine Learning | |
| CompF-128 | Topic Areas CompF03, IF04, IF07 | |
| CompF-129 | Differentiable Simulators for HEP | |
| CompF-130 | Particle Physics and Machine Learning in Education | |
| CompF-131 | Machine Learning and Neural Networks for Field Theory | |
| CompF-131 | Machine Learning and Lattice QCD | |
| EF-028 | Anomaly Detection at Future Colliders | |
| EF-140 | Jets and Jet Substructure at Future Colliders | |
| EF-141 | Optimization of High Energy Physics Analysis Performance | |
| EF-153 | Jet Physics at the Electron Ion Collider | |
| EF-159 | The Femtography Project | |
| EF-189 | Generative, Explainable Artificial | |
| EF-195 | Jets and jet substructure in heavy-ion collisions | |
| EF-205 | Hadronic Tomography at the EIC and the Energy Frontier | |
| IF-072 | Self-driving data trigger, filtering, and acquisition | |
| IF-132 | Real-time adaptive deep-learning with embedded | |
| IF-167 | Data Acquisition and Trigger Enhancements for Low-Energy Events in DUNE | |
| NF-152 | ντ Reconstruction in the Deep Underground Neutrino Experiment | |
| NF-191 | Scalable, End-to-End Optimizable Data | |
| TF-048 | CMB-Like Observable Scheme for Collider Searches | |
| TF-049 | Topological Aspects of Jets and Events at Colliders | |
| TF-086 | Detecting New Physics as Novelty | |
| UF-006 | Advanced Germanium Detectors and Technologies for Underground Physics | |