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Inverse boundary problem for a mean field game system with probability density constraint
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By following the study in [24], we consider an inverse boundary problem for the mean field game system where a probability density constraint is enforced on the game agents. That is, we consider the case that reflective boundary conditions are enforced and hence the population distribution of the game agents should be treated as a probability measure which preserves both positivity and the total population. This poses significant challenges for the corresponding inverse problems in constructing suitable ``probing modes" which should fulfill such a probability density constraint. We develop an effective scheme in tackling such a case which is new to the literature.
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Simultaneously decoding the unknown stationary state and function parameters for mean field games
Under restrictive admissibility conditions, a quadratic time-dependent mean field game is uniquely identifiable from full lateral boundary Cauchy data of its perturbed stationary states.
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