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.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Reduces elliptic triple outcome model to one free parameter, matches N-body simulations except at low angular momentum, and finds observably eccentric merger fractions of 2.6-4.4% in 10^5-10^7 solar mass clusters.
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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.