Unique recovery of nonlinear edge potentials in sheaf-governed multi-agent systems is possible if and only if sheaf cohomology vanishes, or when a data-dependent information matrix is positive definite in parameterized classes.
Learning con- sistent causal abstraction networks
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Multi-Agent System Identification with Nonlinear Sheaf Diffusion
Unique recovery of nonlinear edge potentials in sheaf-governed multi-agent systems is possible if and only if sheaf cohomology vanishes, or when a data-dependent information matrix is positive definite in parameterized classes.