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Simultaneously recovering running cost and Hamiltonian in Mean Field Games system

Hongyu Liu, Shen Zhang

Abstract

We propose and study several inverse problems for the mean field games (MFG) system in a bounded domain. Our focus is on simultaneously recovering the running cost and the Hamiltonian within the MFG system by the associated boundary observation. There are several technical novelties that make the study intriguing and challenging. First, the MFG system couples two nonlinear parabolic PDEs with one moving forward and the other one moving backward in time. Second, there is a probability density constraint on the population distribution of the agents. Third, the simultaneous recovery of two coupling factors within the MFG system is technically far from being trivial. Fourth, we consider both cases that the running cost depends on the population density locally and non-locally, and the two cases present different technical challenges for the inverse problem study. We develop two mathematical strategies that can ensure the probability constraint as well as effectively tackle the inverse problems, which are respectively termed as high-order variation and successive linearisation. In particular, the high-order variation method is new to the literature, which demonstrates a novel concept to examine the inverse problems by non-negative inputs only. We believe the methods developed can find applications to inverse problems in other contexts.

Simultaneously recovering running cost and Hamiltonian in Mean Field Games system

Abstract

We propose and study several inverse problems for the mean field games (MFG) system in a bounded domain. Our focus is on simultaneously recovering the running cost and the Hamiltonian within the MFG system by the associated boundary observation. There are several technical novelties that make the study intriguing and challenging. First, the MFG system couples two nonlinear parabolic PDEs with one moving forward and the other one moving backward in time. Second, there is a probability density constraint on the population distribution of the agents. Third, the simultaneous recovery of two coupling factors within the MFG system is technically far from being trivial. Fourth, we consider both cases that the running cost depends on the population density locally and non-locally, and the two cases present different technical challenges for the inverse problem study. We develop two mathematical strategies that can ensure the probability constraint as well as effectively tackle the inverse problems, which are respectively termed as high-order variation and successive linearisation. In particular, the high-order variation method is new to the literature, which demonstrates a novel concept to examine the inverse problems by non-negative inputs only. We believe the methods developed can find applications to inverse problems in other contexts.
Paper Structure (14 sections, 7 theorems, 118 equations, 1 table)

This paper contains 14 sections, 7 theorems, 118 equations, 1 table.

Table of Contents

  1. Introduction
  2. Mathematical setup
  3. Background and motivation
  4. Discussion on technical developments
  5. Preliminaries and statement of main results
  6. Admissible class
  7. Main unique identifiability results
  8. Local well-posedness of the forward problems
  9. Analysis of the linearized systems in two different scenarios
  10. Higher-order linearization
  11. High-order variation
  12. Proofs of Theorems \ref{['der F']} and \ref{['der F2']}
  13. The main proofs
  14. Comparison and discussion of the two linearisation methods

Key Result

Theorem 2.1

Assume that $F_j(x,m)\in\mathcal{A}$. Let $\mathcal{N}_{F_j,H_j}$, $j=1,2$, be the measurement map associated to the following system: If for any $(m_0,\psi)\in [ C^{2+\alpha}(\Omega) \cap \mathcal{O}]\times C^{2+\alpha}(\Omega)$, where $\mathcal{O}$ is defined in eq:distr1, one has then it holds that and

Theorems & Definitions (17)

  • Definition 2.1
  • Definition 2.2
  • Remark 2.3
  • Theorem 2.1
  • Theorem 2.2
  • Theorem 3.1
  • Theorem 3.2
  • proof : Proof of Theorem $\ref{["local_wellpose1'"]}$
  • Theorem 3.3
  • Theorem 3.4
  • ...and 7 more