Shen Zhang, Michigan State
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PDL C-038
This study explores an inverse problem approach to Mean Field Games
(MFGs) using Cauchy data around unknown stationary states. We propose
a novel method that generalizes MFG formulations, relaxes probability
measure constraints, and introduces high-order linearization around
non-trivial stationary states. These contributions enhance the unique
identifiability of key system parameters, enabling effective model
reconstruction with applications in economics, finance, and
transportation.