A differentiable neural framework for learning state- and time-dependent parameters of finite-state mean field games from population trajectories via implicit differentiation.
arXiv preprint arXiv:2202.06401 , year=
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Global uniqueness with Lipschitz stability for two reaction coefficients and logarithmic stability for initial condition in a nonlinear cell invasion PDE, plus a decoupled numerical reconstruction method.
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Neural Parameter Calibration for Finite-State Mean Field Games
A differentiable neural framework for learning state- and time-dependent parameters of finite-state mean field games from population trajectories via implicit differentiation.