# Real-Time Gravitational-Wave Inference with Probabilistic Machine Learning

- Institution: CERN
- Speaker: Maximilian Dax
- Year: 2026
- Language: English
- Subjects: Computer science
- Source: [CERN](https://videos.cern.ch/record/3025583)
- Licence: [CERN](https://copyright.web.cern)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

Gravitational-wave (GW) astronomy promises groundbreaking discoveries in the coming decades, but its progress is bottlenecked by the computational challenges of large-scale and real-time data analysis. I will present DINGO, a machine learning approach for fast and accurate GW inference that addresses these challenges. DINGO trains generative neural networks to directly estimate probability distributions over GW source parameters. I will explain the core ideas behind DINGO and highlight several machine learning techniques that we developed to adapt modern simulation-based inference to the challenging field of GW data analysis.

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## Lectures

1. [Real-Time Gravitational-Wave Inference with Probabilistic Machine Learning](https://invisiblenetwork.ai/learn/cern/3025583/7c6e7f794baa), 21 min (CERN's player)