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数値解析セミナー
過去の記録 ～09/19｜次回の予定｜今後の予定 09/20～
| 開催情報 | 火曜日 16:30～18:00 数理科学研究科棟(駒場) 002号室 |
|---|---|
| 担当者 | 齊藤宣一、柏原崇人 |
| セミナーURL | https://sites.google.com/g.ecc.u-tokyo.ac.jp/utnas-bulletin-board/ |
次回の予定
2026年10月06日(火)
16:30-18:00 数理科学研究科棟(駒場) 002号室
Yohance Osborne 氏 (Durham University)
Numerical approximation of mean field games systems (English)
https://sites.google.com/g.ecc.u-tokyo.ac.jp/utnas-bulletin-board/
Yohance Osborne 氏 (Durham University)
Numerical approximation of mean field games systems (English)
[ 講演概要 ]
Mean field games (MFG) are models for Nash equilibria of competitive differential games of optimal control involving a continuum of players. MFG equilibria are often described by systems of partial differential equations, where a Hamilton–Jacobi–Bellman (HJB) equation for the representative player’s value function is coupled nonlinearly with a Kolmogorov–Fokker–Planck (KFP) equation that determines the player distribution. MFG systems are particularly challenging to approximate computationally because they exhibit the following difficulties all at once: nonlinear couplings between the HJB and KFP equations, a temporal forward–backward structure, non-negativity of absolutely continuous player distributions, as well as potential nondifferentiability of the Hamiltonian. This talk addresses the numerical approximation of MFG systems for two classes of numerical schemes: (1) monotone stabilized finite element methods for second-order MFG systems with nondifferentiable Hamiltonians, and (2) particle methods for first-order MFG systems under displacement monotonicity. I will present theorems on the strong convergence of these numerical schemes and their rates of convergence, along with discussion of some numerical experiments.
[ 参考URL ]Mean field games (MFG) are models for Nash equilibria of competitive differential games of optimal control involving a continuum of players. MFG equilibria are often described by systems of partial differential equations, where a Hamilton–Jacobi–Bellman (HJB) equation for the representative player’s value function is coupled nonlinearly with a Kolmogorov–Fokker–Planck (KFP) equation that determines the player distribution. MFG systems are particularly challenging to approximate computationally because they exhibit the following difficulties all at once: nonlinear couplings between the HJB and KFP equations, a temporal forward–backward structure, non-negativity of absolutely continuous player distributions, as well as potential nondifferentiability of the Hamiltonian. This talk addresses the numerical approximation of MFG systems for two classes of numerical schemes: (1) monotone stabilized finite element methods for second-order MFG systems with nondifferentiable Hamiltonians, and (2) particle methods for first-order MFG systems under displacement monotonicity. I will present theorems on the strong convergence of these numerical schemes and their rates of convergence, along with discussion of some numerical experiments.
https://sites.google.com/g.ecc.u-tokyo.ac.jp/utnas-bulletin-board/
