Abstract
This paper is devoted to proving the strong averaging principle for slow–fast stochastic partial differential equations with locally monotone coefficients, where the slow component is a stochastic partial differential equations with locally monotone coefficients and the fast component is a stochastic partial differential equations with strongly monotone coefficients. The result is applicable to a large class of examples, such as the stochastic porous medium equation, the stochastic p-Laplace equation, the stochastic Burgers type equation and the stochastic 2D Navier–Stokes equation, which are the nonlinear stochastic partial differential equations. The main techniques are based on time discretization and the variational approach to stochastic partial differential equations.
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References
Bertram, R., Rubin, J.E.: Multi-timescale systems and fast-slow analysis. Math. Biosci. 287, 105–121 (2017)
Bogoliubov, N.N., Mitropolsky, Y.A.: Asymptotic Methods in the Theory of Non-linear Oscillations. Gordon and Breach Science Publishers, New York (1961)
Bréhier, C.E.: Strong and weak orders in averaging for SPDEs. Stoch. Process. Appl. 122, 2553–2593 (2012)
Cerrai, S.: A Khasminskii type averaging principle for stochastic reaction-diffusion equations. Ann. Appl. Probab. 19, 899–948 (2009)
Cerrai, S.: Averaging principle for systems of reaction-diffusion equations with polynomial nonlinearities perturbed by multiplicative noise. SIAM J. Math. Anal. 43, 2482–2518 (2011)
Cerrai, S., Freidlin, M.: Averaging principle for stochastic reaction-diffusion equations. Probab. Theory Relat. Fields 144, 137–177 (2009)
Cerrai, S., Lunardi, A.: Averaging principle for nonautonomous slow-fast systems of stochastic reaction-diffusion equations: the almost periodic case. SIAM J. Math. Anal. 49, 2843–2884 (2017)
Chueshov, I., Millet, A.: Stochastic 2D hydrodynamical type systems: well posedness and large deviations. Appl. Math. Optim. 61, 379–420 (2010)
Dong, Z., Sun, X., Xiao, H., Zhai, J.: Averaging principle for one dimensional stochastic Burgers equation. J. Differ. Equ. 265, 4749–4797 (2018)
E, W., Engquist, B.: Multiscale modeling and computations. Not. AMS 50, 1062–1070 (2003)
E, W., Liu, D., Vanden-Eijnden, E.: Analysis of multiscale methods for stochastic differential equations. Commun. Pure Appl. Math 58, 1544–1585 (2005)
Fu, H., Liu, J.: Strong convergence in stochastic averaging principle for two time-scales stochastic partial differential equations. J. Math. Anal. Appl. 384, 70–86 (2011)
Fu, H., Wan, L., Liu, J.: Strong convergence in averaging principle for stochastic hyperbolic-parabolic equations with two time-scales. Stoch. Process. Appl. 125, 3255–3279 (2015)
Gao, P.: Averaging principle for stochastic Kuramoto-Sivashinsky equation with a fast oscillation. Discret. Contin. Dyn. Syst. A 38, 5649–5684 (2018)
Gao, P.: Averaging principle for the higher order nonlinear Schrödinger equation with a random fast oscillation. J. Stat. Phys. 171, 897–926 (2018)
Gao, P.: Averaging principle for multiscale stochastic Klein-Gordon-heat system. J. Nonlinear Sci. 29(4), 1701–1759 (2019)
Harvey, E., Kirk, V., Wechselberger, M., Sneyd, J.: Multiple timescales, mixed mode oscillations and canards in models of intracellular calcium dynamics. J. Nonlinear Sci. 21, 639–683 (2011)
Khasminskii, R.Z.: On the principle of averaging the Itô stochastic differential equations. Kibernetica 4, 260–279 (1968)
Li, S., Sun, X., Xie, Y., Zhao, Y.: Averaging principle for two dimensional stochatsic Navier–Stokes equations. arXiv:1810.02282
Liu, W., Röckner, M.: Stochastic Partial Differential Equations: An Introduction. Springer, Berlin (2015)
Liu, W., Röckner, M., Sun, X., Xie, Y.: Averaging principle for slow-fast stochastic differential equations with time dependent locally Lipschitz coefficients. J. Differ. Equ. 268(6), 2910–2948 (2020)
Mastny, E.A., Haseltine, E.L., Rawlings, J.B.: Two classes of quasi-steady-state model reductions for stochastic kinetics. J. Chem. Phys. 127, 094106 (2007)
Øksendal, B.: Stochastic Differential Equations. An Introduction with Application. Springer, Berlin (1995)
Wang, W., Roberts, A.J.: Average and deviation for slow-fast stochastic partial differential equations. J. Differ. Equ. 253, 1265–1286 (2012)
Wang, W., Roberts, A.J., Duan, J.: Large deviations and approximations for slow-fast stochastic reaction-diffusion equations. J. Differ. Equ. 253, 3501–3522 (2012)
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The authors would like to thank the referees for some valuable comments. This work is supported by the National Natural Science Foundation of China (Nos. 11931004, 12171208, 11831014, 12271219, 12090011), the QingLan Project of Jiangsu Provience and the PAPD Project of Jiangsu Higher Education Institutions. Financial support of the DFG through CRC 1283 is also gratefully acknowledged.
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Appendix
Appendix
At the end of this section, we give the proof of Theorem 2.3 based on the techniques used in [20, Theorem 5.1.3].
Proof of Theorem 2.3
Let \(\mathcal {H}:={H_1}\times H_2\) be the product Hilbert space. For any \(\phi =(\phi _1,\phi _2),\varphi =(\varphi _1,\varphi _2)\in \mathcal {H}\), we denote the scalar product and the induced norm by
Similarly, we also define \(\mathcal {V}:=V_1\times V_2\). Then \(\mathcal {V}\) is a reflexive Banach space with the following norm:
Now we rewrite the system (1.1) for \(Z^{\varepsilon }_t=(X^{\varepsilon }_t,Y^{\varepsilon }_t)\) as
where \(W_t:=(W_t^{1},W_t^{2})\), which is a \(U_1\times U_2\)-valued cylindrical-Wiener process and
Moreover, G is an operator from \(\mathcal {V}\) to \(L_{2}(\mathcal {U},\mathcal {H})\), where \(\mathcal {U}:=U_1\times U_2\) and \(L_{2}(\mathcal {U},\mathcal {H})\) is the space of Hilbert-Schmidt operators from \(\mathcal {U}\) to \(\mathcal {H}\). The norm in \(L_{2}(\mathcal {U},\mathcal {H})\) is defined by
Let \(\mathcal {V}^{*}\) be the dual space of \(\mathcal {V}\) and we consider the following Gelfand triple \(\mathcal {V}\subset \mathcal {H}\equiv \mathcal {H}^{*}\subset \mathcal {V}^{*}\). It is easy to see that the following mappings
are well defined. To complete the proof, we only check whether the new coefficients in equation (5.1) satisfy the local monotonicity, coercivity and growth properties by [20, Theorem 5.1.3].
Indeed, for any \(w_1=(u_1,v_1),w_2=(u_2,v_2)\in \mathcal {V}\), by conditions A2 and B2, we have
which implies that the local monotonicity condition holds.
For any \(w=(u,v)\in \mathcal {V}\), there exist constants \(C_{{\varepsilon }}>0\) and \(C>0\) such that
and
for some \(\tilde{\beta }> 0\), which implies that the coercivity and growth conditions hold. \(\square \)
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Liu, W., Röckner, M., Sun, X. et al. Strong Averaging Principle for Slow–Fast Stochastic Partial Differential Equations with Locally Monotone Coefficients. Appl Math Optim 87, 39 (2023). https://doi.org/10.1007/s00245-022-09956-y
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DOI: https://doi.org/10.1007/s00245-022-09956-y