Papers

• Generative Unfolding with Distribution Mapping (2024)
  Anja Butter et al.
  7.02
• Fast, accurate, and differentiable: a neural-network surrogate for NRSur7dq4 precessing binary black hole waveforms (2026)
  Michael P\"urrer et al.
  2.00
• Normalizing Flows to Reconstruct Pseudo-PDFs (2026)
  Yamil Cahuana Medrano et al.
  2.00
• ML-based Fast Simulation of FARICH Responses (2026)
  Foma Shipilov et al.
  1.89
• An End-to-end Architecture for Collider Physics and Beyond (2026)
  Shi Qiu et al.
  1.78
• Extrapolation to infinite model space of no-core shell model calculations using machine learning (2025)
  Aleksandr Mazur et al.
  1.56
• Improved Ground State Estimation in Quantum Field Theories via Normalising Flow-Assisted Neural Quantum States (2025)
  Vishal S. Ngairangbam et al.
  1.28
• Physics consistent machine learning framework for inverse modeling with applications to ICF capsule implosions (2024)
  Daniel A. Serino et al.
  1.00
• Neural Networks as Effective Surrogate Models of Radio-Frequency Quadrupole Particle Accelerator Simulations (2022)
  Joshua Villarreal and Daniel Winklehner and Daniel Koser and Janet Marie Conrad
  —
• TURBO: The Swiss Knife of Auto-Encoders (2023)
  Guillaume Qu\'etant et al.
  —
• Accelerating HEP simulations with Neural Importance Sampling (2024)
  Nicolas Deutschmann and Niklas G\"otz
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• PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction (2024)
  Junjian Lu et al.
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• Machine-learning emergent spacetime from linear response in future tabletop quantum gravity experiments (2024)
  Koji Hashimoto et al.
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