Flow Matching | A Quest After Perspectiveshttps://iphysresearch.github.io/blog/tag/flow-matching/Flow MatchingHugo Blox Builder (https://hugoblox.com)en-usFri, 03 Apr 2026 00:00:00 +0000https://iphysresearch.github.io/blog/media/sharing.pngFlow Matchinghttps://iphysresearch.github.io/blog/tag/flow-matching/FluxMC: Rapid and High-Fidelity Inference for Space-Based Gravitational-Wave Observationshttps://iphysresearch.github.io/blog/mypublication/2026_fluxmc/Fri, 03 Apr 2026 00:00:00 +0000https://iphysresearch.github.io/blog/mypublication/2026_fluxmc/<p>Benchmarks cover multimodal Gaussian targets and massive-black-hole-binary signals with IMRPhenomD and IMRPhenomHM waveforms. Flow-generated global proposals complement parallel-tempering exploration while the Monte Carlo step preserves asymptotic sampling correctness.</p>Unlocking New Paths for Efficient Analysis of Gravitational Waves from Extreme-Mass-Ratio Inspirals with Machine Learninghttps://iphysresearch.github.io/blog/mypublication/2025_emri_cpl/Thu, 31 Jul 2025 00:00:00 +0000https://iphysresearch.github.io/blog/mypublication/2025_emri_cpl/<p>The method targets the computational difficulty of EMRI posterior estimation. The published study notes that learned distributions can require refinement with conventional Bayesian sampling.</p> <h2 id="sources">Sources</h2> <ul> <li><a href="https://cpl.iphy.ac.cn/article/doi/10.1088/0256-307X/42/8/081101?viewType=HTML" target="_blank" rel="noopener">Chinese Physics Letters article and publication history</a></li> <li><a href="https://iopscience.iop.org/article/10.1088/0256-307X/42/8/081101" target="_blank" rel="noopener">Publisher record</a></li> <li><a href="https://api.crossref.org/works/10.1088/0256-307X/42/8/081101" target="_blank" rel="noopener">Publisher metadata via Crossref</a></li> </ul>