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Invariant Measures for the Nonlinear Stochastic Heat Equation with No Drift Term

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Abstract

This paper deals with the long-term behavior of the solution to the nonlinear stochastic heat equation \(\frac{\partial u}{\partial t} - \frac{1}{2}\Delta u = b(u){\dot{W}}\), where b is assumed to be a globally Lipschitz continuous function and the noise \({\dot{W}}\) is a centered and spatially homogeneous Gaussian noise that is white in time. We identify a set of nearly optimal conditions on the initial data, the correlation measure of the noise, and the weight function \(\rho \), which together guarantee the existence of an invariant measure in the weighted space \(L^2_\rho ({\mathbb {R}}^d)\). In particular, our result covers the parabolic Anderson model (i.e., the case when \(b(u) = \lambda u\)) starting from the Dirac delta measure.

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Notes

  1. In [17], the authors constructed a spectral measure \({\widehat{f}}\) such that \(\Upsilon (0)<\infty \) and \(\Upsilon _\alpha (\beta ) = \infty \) for all \(\alpha \in (0,1)\).

References

  1. Chen, L., Dalang, R.C.: Moments and growth indices for the nonlinear stochastic heat equation with rough initial conditions. Ann. Probab. 43(6), 3006–3051 (2015). https://doi.org/10.1214/14-AOP954

    Article  MathSciNet  Google Scholar 

  2. Chen, L., Kim, K.: Nonlinear stochastic heat equation driven by spatially colored noise: moments and intermittency. Acta Math. Sci. Ser. B (Engl. Ed.) 39(3), 645–668 (2019). https://doi.org/10.1007/s10473-019-0303-6

    Article  MathSciNet  Google Scholar 

  3. Chen, L., Huang, J.: Comparison principle for stochastic heat equation on \(\mathbb{R} ^d\). Ann. Probab. 47(2), 989–1035 (2019). https://doi.org/10.1214/18-AOP1277

    Article  MathSciNet  Google Scholar 

  4. Carmona, R.A., Molchanov, S.A.: Parabolic Anderson problem and intermittency. Mem. Am. Math. Soc. 108(518), 125 (1994). https://doi.org/10.1090/memo/0518

    Article  MathSciNet  Google Scholar 

  5. Dalang, R., Khoshnevisan, D., Mueller, C., Nualart, D., Xiao, Y.: A Minicourse on Stochastic Partial Differential Equations. Lecture Notes in Mathematics, vol. 1962, p. 216. Springer, (2009). Held at the University of Utah, Salt Lake City, UT, May 8–19, 2006, Edited by Khoshnevisan and Firas Rassoul-Agha

  6. Dalang, R.C.: Extending the martingale measure stochastic integral with applications to spatially homogeneous S.P.D.E.’s. Electron. J. Probab. 4, 6–29 (1999). https://doi.org/10.1214/EJP.v4-43

    Article  MathSciNet  Google Scholar 

  7. Walsh, J.B.: An introduction to stochastic partial differential equations. In: École D’été de Probabilités de Saint-Flour, XIV—1984. Lecture Notes in Math., vol. 1180, pp. 265–439. Springer (1986). https://doi.org/10.1007/BFb0074920

  8. Cerrai, S.: Second Order PDE’s in Finite and Infinite Dimension. Lecture Notes in Mathematics. A Probabilistic Approach, vol. 1762, p. 330. Springer (2001). https://doi.org/10.1007/b80743

  9. Da Prato, G., Zabczyk, J.: Ergodicity for Infinite-Dimensional Systems. London Mathematical Society Lecture Note Series, vol. 229, p. 339. Cambridge University Press, Cambridge (1996). https://doi.org/10.1017/CBO9780511662829

    Book  Google Scholar 

  10. Da Prato, G., Zabczyk, J.: Stochastic equations in infinite dimensions. In: Encyclopedia of Mathematics and Its Applications, 2nd edn, vol. 152, p. 493. Cambridge University Press, Cambridge (2014). https://doi.org/10.1017/CBO9781107295513

  11. Tessitore, G., Zabczyk, J.: Invariant measures for stochastic heat equations. Probab. Math. Stat. 18(2, Acta Univ. Wratislav. No. 2111), 271–287 (1998)

  12. Gu, Y., Li, J.: Fluctuations of a nonlinear stochastic heat equation in dimensions three and higher. SIAM J. Math. Anal. 52(6), 5422–5440 (2020). https://doi.org/10.1137/19M1296380

    Article  MathSciNet  Google Scholar 

  13. Dunlap, A., Gu, Y., Ryzhik, L., Zeitouni, O.: The random heat equation in dimensions three and higher: the homogenization viewpoint. Arch. Ration. Mech. Anal. 242(2), 827–873 (2021). https://doi.org/10.1007/s00205-021-01694-9

    Article  MathSciNet  Google Scholar 

  14. Foondun, M., Khoshnevisan, D.: Intermittence and nonlinear parabolic stochastic partial differential equations. Electron. J. Probab. 14, 21–548568 (2009). https://doi.org/10.1214/EJP.v14-614

    Article  MathSciNet  Google Scholar 

  15. Dalang, R.C., Quer-Sardanyons, L.: Stochastic integrals for SPDE’s: a comparison. Expo. Math. 29(1), 67–109 (2011). https://doi.org/10.1016/j.exmath.2010.09.005

    Article  MathSciNet  Google Scholar 

  16. Amir, G., Corwin, I., Quastel, J.: Probability distribution of the free energy of the continuum directed random polymer in \(1+1\) dimensions. Commun. Pure Appl. Math. 64(4), 466–537 (2011). https://doi.org/10.1002/cpa.20347

    Article  MathSciNet  Google Scholar 

  17. Chen, L., Huang, J., Khoshnevisan, D., Kim, K.: Dense blowup for parabolic SPDEs. Electron. J. Probab. 24, 118–33 (2019). https://doi.org/10.1214/19-ejp372

    Article  MathSciNet  Google Scholar 

  18. Chen, L., Kim, K.: Stochastic comparisons for stochastic heat equation. Electron. J. Probab. 25, 140–38 (2020). https://doi.org/10.1214/20-ejp541

    Article  MathSciNet  Google Scholar 

  19. Joseph, M., Khoshnevisan, D., Mueller, C.: Strong invariance and noise-comparison principles for some parabolic stochastic PDEs. Ann. Probab. 45(1), 377–403 (2017). https://doi.org/10.1214/15-AOP1009

    Article  MathSciNet  Google Scholar 

  20. Sanz-Solé, M., Sarrà, M.: Hölder continuity for the stochastic heat equation with spatially correlated noise. In: Seminar on Stochastic Analysis, Random Fields and Applications, III (Ascona, 1999). Progress in Probabability, vol. 52, pp. 259–268. Birkhäuser, Basel, (2002)

  21. Peszat, S., Zabczyk, J.: Stochastic evolution equations with a spatially homogeneous Wiener process. Stoch. Process. Appl. 72(2), 187–204 (1997). https://doi.org/10.1016/S0304-4149(97)00089-6

    Article  MathSciNet  Google Scholar 

  22. Balan, R.M., Chen, L.: Parabolic Anderson model with space-time homogeneous Gaussian noise and rough initial condition. J. Theor. Probab. 31(4), 2216–2265 (2018). https://doi.org/10.1007/s10959-017-0772-2

    Article  MathSciNet  Google Scholar 

  23. Da Prato, G., Kwapień, S., Zabczyk, J.: Regularity of solutions of linear stochastic equations in Hilbert spaces. Stochastics 23(1), 1–23 (1987). https://doi.org/10.1080/17442508708833480

    Article  MathSciNet  Google Scholar 

  24. Olver, F.W.J., Lozier, D.W., Boisvert, R.F., Clark, C.W.: NIST Handbook of Mathematical Functions, p. 951. U.S. Department of Commerce, National Institute of Standards and Technology, Washington, DC; Cambridge University Press, Cambridge (2010) With 1 CD-ROM. (Windows, Macintosh and UNIX)

  25. Billingsley, P.: Convergence of probability measures. In: Wiley Series in Probability and Statistics: Probability and Statistics. A Wiley-Interscience Publication, 2nd edn, p. 277. Wiley, New York (1999). https://doi.org/10.1002/9780470316962

  26. Assing, S., Manthey, R.: Invariant measures for stochastic heat equations with unbounded coefficients. Stoch. Process. Appl. 103(2), 237–256 (2003). https://doi.org/10.1016/S0304-4149(02)00211-9

    Article  MathSciNet  Google Scholar 

  27. Brzeźniak, Za., Ga̧tarek, D.: Martingale solutions and invariant measures for stochastic evolution equations in Banach spaces. Stoch. Process. Appl. 84(2), 187–225 (1999). https://doi.org/10.1016/S0304-4149(99)00034-4

    Article  MathSciNet  Google Scholar 

  28. Cerrai, S.: Stochastic reaction–diffusion systems with multiplicative noise and non-Lipschitz reaction term. Probab. Theory Relat. Fields 125(2), 271–304 (2003). https://doi.org/10.1007/s00440-002-0230-6

    Article  MathSciNet  Google Scholar 

  29. Eckmann, J.-P., Hairer, M.: Invariant measures for stochastic partial differential equations in unbounded domains. Nonlinearity 14(1), 133–151 (2001). https://doi.org/10.1088/0951-7715/14/1/308

    Article  MathSciNet  ADS  Google Scholar 

  30. Misiats, O., Stanzhytskyi, O., Yip, N.K.: Existence and uniqueness of invariant measures for stochastic reaction–diffusion equations in unbounded domains. J. Theor. Probab. 29(3), 996–1026 (2016). https://doi.org/10.1007/s10959-015-0606-z

    Article  MathSciNet  Google Scholar 

  31. Misiats, O., Stanzhytskyi, O., Yip, N.K.: Invariant measures for stochastic reaction-diffusion equations with weakly dissipative nonlinearities. Stochastics 92(8), 1197–1222 (2020). https://doi.org/10.1080/17442508.2019.1691212

    Article  MathSciNet  Google Scholar 

  32. Grafakos, L.: Modern Fourier analysis. In: Graduate Texts in Mathematics, 3rd edn, vol. 250, p. 624. Springer (2014). https://doi.org/10.1007/978-1-4939-1230-8

  33. Loh, W.-L., Sun, S., Wen, J.: On fixed-domain asymptotics, parameter estimation and isotropic Gaussian random fields with Matérn covariance functions. Ann. Stat. 49(6), 3127–3152 (2021). https://doi.org/10.1214/21-aos2077

    Article  Google Scholar 

  34. Stein, M.L.: Interpolation of spatial data. In: Springer Series in Statistics. Some Theory for Kriging, p. 247. Springer (1999). https://doi.org/10.1007/978-1-4612-1494-6

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Acknowledgements

L.C. thanks Sandra Cerrai for some helpful discussions and for pointing out the reference [30] during the conference Frontier Probability Days 2018. L.C. also thanks Yu Gu for some helpful discussions regarding the stationary limit obtained in [12] during the Workshop: Stochastic PDEs & Related Topics, Nov. 14–16, 2022 at Brin Mathematics Research Center, University of Maryland, and for pointing out the reference [13]. Both authors would like to thank the anonymous referee for helpful comments and suggestions.

Funding

Le Chen is partially supported by NSF Grant DMS-2246850 and a collaboration grant from the Simons Foundation (# 959981).

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Chen, L., Eisenberg, N. Invariant Measures for the Nonlinear Stochastic Heat Equation with No Drift Term. J Theor Probab (2024). https://doi.org/10.1007/s10959-023-01302-4

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