{"total":20,"rows":[{"id":"2203.12634","title":"Applications of physics informed neural operators","created":"2022-03-23","cited_by_count":33,"area":null},{"id":"2108.02214","title":"A FAIR and AI-ready Higgs boson decay dataset","created":"2021-08-04","cited_by_count":14,"area":null},{"id":"2210.08973","title":"FAIR for AI: An interdisciplinary and international community building perspective","created":"2022-09-30","cited_by_count":13,"area":null},{"id":"2105.06479","title":"Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources","created":"2021-05-13","cited_by_count":10,"area":null},{"id":"2207.00611","title":"FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy","created":"2022-07-01","cited_by_count":10,"area":null},{"id":"2403.13858","title":"A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators","created":"2024-03-19","cited_by_count":8,"area":null},{"id":"2107.12698","title":"Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders","created":"2021-07-27","cited_by_count":7,"area":null},{"id":"2408.07847","title":"Time-inversion of spatiotemporal beam dynamics using uncertainty-aware latent evolution reversal","created":"2024-08-14","cited_by_count":6,"area":null},{"id":"2110.06968","title":"Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers","created":"2021-10-13","cited_by_count":5,"area":null},{"id":"2201.11133","title":"Inference-optimized AI and high performance computing for gravitational wave detection at scale","created":"2022-01-26","cited_by_count":5,"area":null},{"id":"2209.11146","title":"MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge","created":"2022-09-22","cited_by_count":4,"area":null},{"id":"2212.05081","title":"FAIR AI Models in High Energy Physics","created":"2022-12-09","cited_by_count":4,"area":null},{"id":"2112.07669","title":"AI and extreme scale computing to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers","created":"2021-12-13","cited_by_count":3,"area":null},{"id":"2405.04161","title":"Decoding complexity: how machine learning is redefining scientific discovery","created":"2024-05-07","cited_by_count":3,"area":null},{"id":"2202.07399","title":"Interpreting a Machine Learning Model for Detecting Gravitational Waves","created":"2022-02-15","cited_by_count":2,"area":null},{"id":"2412.19883","title":"A Neural Network-Based Search for Unmodeled Transients in LIGO-Virgo-KAGRA's Third Observing Run","created":"2024-12-27","cited_by_count":1,"area":null},{"id":"2605.02453","title":"Testing General Relativity Through Gravitational Wave Classification: A Convolutional Neural Network Framework","created":"2026-05-04","cited_by_count":1,"area":null},{"id":"2310.00052","title":"AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers","created":"2023-09-29","cited_by_count":0,"area":null},{"id":"2410.11911","title":"Transfer Learning Adapts to Changing PSD in Gravitational Wave Data","created":"2024-10-15","cited_by_count":0,"area":null},{"id":"2607.01372","title":"AI-enabled gravitational-waves searches for binary neutron stars at optimal sensitivity","created":"2026-07-01","cited_by_count":0,"area":null}],"next_cursor":null}