In this Review, we review some recent rigorous results on large N problems in quantum field theory, stochastic quantization, and singular stochastic partial differential equations (SPDEs) and their mean field limit problems. In particular, we discuss the O(N) linear sigma model on a two- and three-dimensional torus. The stochastic quantization procedure leads to a coupled system of N interacting Φ4 equations. In d = 2, we show uniformity in N bounds for the dynamics and convergence to a mean-field singular SPDE. For large enough mass or small enough coupling, the invariant measures [i.e., the O(N) linear sigma model] converge to the massive Gaussian free field, the unique invariant measure of the mean-field dynamics, in a Wasserstein distance. We also obtain tightness for certain O(N) invariant observables as random fields in suitable Besov spaces as N → ∞, along with exact descriptions of the limiting correlations. In d = 3, the estimates become more involved since the equation is more singular. We discuss in this case how to prove convergence to the massive Gaussian free field. The proofs of these results build on the recent progress of singular SPDE theory and combine many new techniques, such as uniformity in N estimates and dynamical mean field theory. These are based on joint papers with Scott Smith, Rongchan Zhu, and Xiangchan Zhu.
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Remark that in this Review, we will not discuss phase transition or spontaneous symmetry breaking although it is an interesting topic.
We will have more precise discussion on Wick renormalization in Sec. IV.
One even simpler example in stochastic ordinary differential equations is given by the Ornstein–Uhlenbeck process , where Bt is the Brownian motion, and its invariant measure is the (one-dimensional) Gaussian measure .
In the context of SDE systems, one also considers the empirical measures of the particle configurations and aims to show their convergence as N → ∞ to the McKean–Vlasov PDEs, which are typically deterministic. Note that in this Review, we do not consider the “analog” of McKean–Vlasov PDE (which would be infinite dimensional) in the context of our model.
The renormalization constant here for our SPDE is consistent with the one in QFT, which is well known in physics.
We will assume sufficiently regular initial condition ϕ in this section and focus on roughness of the equation itself for simplicity.
Here, we denote by Cα the Hölder–Besov spaces; see, e.g., Ref. 90, Appendix A for the definitions of these spaces.
Recall that this means that their joint probability distribution does not change under permutations of the components.
The block Δjf is basically the Fourier modes of order 2j of f.
These renormalization constants are the same as in the one-component QFT, namely, aɛ diverges at rate ɛ−1 and diverges logarithmically.