Bi-CFM learns bidirectional mappings between initial and final state distributions to solve ill-posed inverse problems in chaotic systems, reporting metric improvements and speedups on Lorenz variants plus conservation-respecting results on three-body and globular cluster data.
@doi [ ] 10.1146/annurev.aa.33.090195.001025, https://ui.adsabs.harvard.edu/abs/1995ARA&A..33..133B 33
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Solving Inverse Problems of Chaotic Systems with Bidirectional Conditional Flow Matching
Bi-CFM learns bidirectional mappings between initial and final state distributions to solve ill-posed inverse problems in chaotic systems, reporting metric improvements and speedups on Lorenz variants plus conservation-respecting results on three-body and globular cluster data.