October  2020, 13(10): 2735-2750. doi: 10.3934/dcdss.2020224

Exact and numerical solution of stochastic Burgers equations with variable coefficients

School of Mathematical and Statistical Sciences, The University of Texas Rio Grande Valley, 1201 W. University Drive, Edinburg, Texas, 78539, USA

* Corresponding author: Tamer Oraby

Received  February 2019 Revised  July 2019 Published  December 2019

We will introduce exact and numerical solutions to some stochastic Burgers equations with variable coefficients. The solutions are found using a coupled system of deterministic Burgers equations and stochastic differential equations.

Citation: Stephanie Flores, Elijah Hight, Everardo Olivares-Vargas, Tamer Oraby, Jose Palacio, Erwin Suazo, Jasang Yoon. Exact and numerical solution of stochastic Burgers equations with variable coefficients. Discrete & Continuous Dynamical Systems - S, 2020, 13 (10) : 2735-2750. doi: 10.3934/dcdss.2020224
References:
[1]

A. Alabert and I. Gyongy, On numerical approximation of stochastic Burgers' equation, From Stochastic Calculus to Mathematical Finance, Springer, Berlin, (2006), 1–15. doi: 10.1007/978-3-540-30788-4_1.  Google Scholar

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L. BertiniN. Cancrini and G. Jona-Lasinio, The stochastic Burgers equation, Communications in Mathematical Physics, 165 (1994), 211-232.  doi: 10.1007/BF02099769.  Google Scholar

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L. Bertini and G. Giacomin, Stochastic Burgers and KPZ equations from particle systems, Communications in Mathematical Physics, 183 (1997), 571-607.  doi: 10.1007/s002200050044.  Google Scholar

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D. Blomker and A. Jentzen, Galerkin approximations for the stochastic Burgers equation, SIAM Journal of Numerical Analysis, 51 (2013), 694-715.  doi: 10.1137/110845756.  Google Scholar

[5]

J. M. Burgers, A mathematical model illustrating the theory of turbulence, Advances in Applied Mechanics, Academic Press, Inc., New York, N. Y., (1948), 171–199.  Google Scholar

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O. Calin, An Informal Introduction to Stochastic Calculus with Applications, World Scientific Publishing Co. Pte. Ltd., Hackensack, NJ, 2015. doi: 10.1142/9620.  Google Scholar

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G. Casella and R. L. Berger, Statistical Inference, Biometrics, 49 (1993), 320-321.  doi: 10.2307/2532634.  Google Scholar

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P. Catuogno and C. Olivera, Strong solution of the stochastic Burgers equation, Applicable Analysis, 93 (2014), 646-652.  doi: 10.1080/00036811.2013.797074.  Google Scholar

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G. Da PratoA. Debussche and R. Temam, Stochastic Burgers' equation, Nonlinear Differential Equations and Applications, 1 (1994), 389-402.  doi: 10.1007/BF01194987.  Google Scholar

[10]

P. DübenD. HomeierK. JansenD. MesterhazyG. Münster and C. Urbach, Monte Carlo simulations of the randomly forced Burgers equation, EPL Journal, 84 (2008), 1-4.   Google Scholar

[11]

S. Eule and R. Friedrich, A note on the forced Burgers equation, Physics Letters A: General, Atomic and Solid State Physics, 351 (2006), 238-241.  doi: 10.1016/j.physleta.2005.11.019.  Google Scholar

[12]

I. Gyöngy and D. Nualart, On the stochastic Burgers' equation in the real line, The Annals of Probability, 27 (1999), 782-802.  doi: 10.1214/aop/1022677386.  Google Scholar

[13]

M. Hairer and J. Voss, Approximations to the stochastic Burgers equation, Journal of Nonlinear Science, 21 (2011), 897-920.  doi: 10.1007/s00332-011-9104-3.  Google Scholar

[14]

H. HoldenT. LindstrømB. øksendalJ. Ubøe and T.-S. Zhang, The Burgers equation with a noisy force and the stochastic heat equation, Communications in Partial Differential Equations, 19 (1994), 119-141.  doi: 10.1080/03605309408821011.  Google Scholar

[15]

P. E. Kloeden and E. Platen, Numerical Solution of Stochastic Differential Equations, Applications of Mathematics (New York), 23. Springer-Verlag, Berlin, 1992. doi: 10.1007/978-3-662-12616-5.  Google Scholar

[16]

P. Lewis and D. Nualart, Stochastic Burgers' equation on the real line: Regularity and moment estimates, Stochastics, 90 (2018), 1053-1086.  doi: 10.1080/17442508.2018.1478834.  Google Scholar

[17]

E. PereiraE. Suazo and J. Trespalacios, Riccati-Ermakov systems and explicit solutions for variable coefficient reaction-diffusion equations, Applied Mathematics and Computation, 329 (2018), 278-296.  doi: 10.1016/j.amc.2018.01.047.  Google Scholar

[18]

A. Truman and H. Z. Zhao, On stochastic diffusion equations and stochastic Burgers' equations, Journal of Mathematical Physics, 37 (1996), 283-307.  doi: 10.1063/1.531391.  Google Scholar

[19]

E. Weinan, Stochastic hydrodynamics, Current Developments in Mathematics, 2000, Int. Press, Somerville, MA, (2001), 109–147.  Google Scholar

show all references

References:
[1]

A. Alabert and I. Gyongy, On numerical approximation of stochastic Burgers' equation, From Stochastic Calculus to Mathematical Finance, Springer, Berlin, (2006), 1–15. doi: 10.1007/978-3-540-30788-4_1.  Google Scholar

[2]

L. BertiniN. Cancrini and G. Jona-Lasinio, The stochastic Burgers equation, Communications in Mathematical Physics, 165 (1994), 211-232.  doi: 10.1007/BF02099769.  Google Scholar

[3]

L. Bertini and G. Giacomin, Stochastic Burgers and KPZ equations from particle systems, Communications in Mathematical Physics, 183 (1997), 571-607.  doi: 10.1007/s002200050044.  Google Scholar

[4]

D. Blomker and A. Jentzen, Galerkin approximations for the stochastic Burgers equation, SIAM Journal of Numerical Analysis, 51 (2013), 694-715.  doi: 10.1137/110845756.  Google Scholar

[5]

J. M. Burgers, A mathematical model illustrating the theory of turbulence, Advances in Applied Mechanics, Academic Press, Inc., New York, N. Y., (1948), 171–199.  Google Scholar

[6]

O. Calin, An Informal Introduction to Stochastic Calculus with Applications, World Scientific Publishing Co. Pte. Ltd., Hackensack, NJ, 2015. doi: 10.1142/9620.  Google Scholar

[7]

G. Casella and R. L. Berger, Statistical Inference, Biometrics, 49 (1993), 320-321.  doi: 10.2307/2532634.  Google Scholar

[8]

P. Catuogno and C. Olivera, Strong solution of the stochastic Burgers equation, Applicable Analysis, 93 (2014), 646-652.  doi: 10.1080/00036811.2013.797074.  Google Scholar

[9]

G. Da PratoA. Debussche and R. Temam, Stochastic Burgers' equation, Nonlinear Differential Equations and Applications, 1 (1994), 389-402.  doi: 10.1007/BF01194987.  Google Scholar

[10]

P. DübenD. HomeierK. JansenD. MesterhazyG. Münster and C. Urbach, Monte Carlo simulations of the randomly forced Burgers equation, EPL Journal, 84 (2008), 1-4.   Google Scholar

[11]

S. Eule and R. Friedrich, A note on the forced Burgers equation, Physics Letters A: General, Atomic and Solid State Physics, 351 (2006), 238-241.  doi: 10.1016/j.physleta.2005.11.019.  Google Scholar

[12]

I. Gyöngy and D. Nualart, On the stochastic Burgers' equation in the real line, The Annals of Probability, 27 (1999), 782-802.  doi: 10.1214/aop/1022677386.  Google Scholar

[13]

M. Hairer and J. Voss, Approximations to the stochastic Burgers equation, Journal of Nonlinear Science, 21 (2011), 897-920.  doi: 10.1007/s00332-011-9104-3.  Google Scholar

[14]

H. HoldenT. LindstrømB. øksendalJ. Ubøe and T.-S. Zhang, The Burgers equation with a noisy force and the stochastic heat equation, Communications in Partial Differential Equations, 19 (1994), 119-141.  doi: 10.1080/03605309408821011.  Google Scholar

[15]

P. E. Kloeden and E. Platen, Numerical Solution of Stochastic Differential Equations, Applications of Mathematics (New York), 23. Springer-Verlag, Berlin, 1992. doi: 10.1007/978-3-662-12616-5.  Google Scholar

[16]

P. Lewis and D. Nualart, Stochastic Burgers' equation on the real line: Regularity and moment estimates, Stochastics, 90 (2018), 1053-1086.  doi: 10.1080/17442508.2018.1478834.  Google Scholar

[17]

E. PereiraE. Suazo and J. Trespalacios, Riccati-Ermakov systems and explicit solutions for variable coefficient reaction-diffusion equations, Applied Mathematics and Computation, 329 (2018), 278-296.  doi: 10.1016/j.amc.2018.01.047.  Google Scholar

[18]

A. Truman and H. Z. Zhao, On stochastic diffusion equations and stochastic Burgers' equations, Journal of Mathematical Physics, 37 (1996), 283-307.  doi: 10.1063/1.531391.  Google Scholar

[19]

E. Weinan, Stochastic hydrodynamics, Current Developments in Mathematics, 2000, Int. Press, Somerville, MA, (2001), 109–147.  Google Scholar

Figure 1.  Two realizations of the stochastic process in equation (28)
Figure 2.  Two realizations of the stochastic process in equation (29)
Figure 3.  (a) and (b): Two realizations of the stochastic mesh resulting from solving equation (7) with $ C(t) = t+1 $ and $ E(t) = t+2 $ for $ t\in[0,2] $ with $ \Delta t = 0.0408 $ when $ z\in[-1,1] $ with $ \Delta z = .1 $. (c) and (d): Two realizations of the stochastic mesh resulting from solving equation (9) with $ B(t) = \exp(t) $, $ R(t) = 1 $ and $ E(t) = 1 $ for $ t\in[0,2] $ with $ \Delta t = 0.0408 $ when $ z\in[-1,1] $ with $ \Delta z = .1 $. Notice the uniformity over space since the noise is space uniform
Figure 4.  Stencil of the numerical scheme with the realization of the incremental trajectory $ dX_t $ when it is positive (a) and negative (b). The stencil is shown for $ \Delta t = k $ and $ \Delta x = h $ which are fixed
Figure 5.  Two realizations of the stochastic meshes (a) and (b), and their respective simulated numerical solutions over those two meshes (c) and (d)
Figure 6.  Two realizations of the two processes $ Z_t $ and $ \dot{Z}_t $ that solve equation (31) (a) and (b), the stochastic meshes (c) and (d), and their respective simulated numerical solutions over those two meshes (e) and (f)
Figure 7.  The relative frequency of the times the absolute error of the SFEM is smaller than the absolute error of the stochastic mesh (SM) method for the solution of (25) at each pair $ (t_i,z_j) $ for $ i = 0,\ldots,m $ and $ j = 0,\ldots,n $ for (a) (m, n) = (20, 20) giving $ P_{\text{max}} = .059 $, (b) (m, n) = (20, 30) giving $ P_{\text{max}} = .053 $, (c) (m, n) = (30, 20) giving $ P_{\text{max}} = .077 $, (d) (m, n) = (30, 30) giving $ P_{\text{max}} = .0597 $, (e) (m, n) = (40, 20) giving $ P_{\text{max}} = .139 $, (f) (m, n) = (40, 30) giving $ P_{\text{max}} = .087 $
Table 1.  The maximum values of the mean absolute errors over $ [0,1]\times[-1,1] $ for different values of $ n $ and $ m $ show that the stochastic mesh method (SM) is better than the stochastic forward Euler method (SFEM) in overall
n m MMAE for SM MMAE for SFEM
20 20 0.032 0.047
20 30 0.033 0.042
20 40 0.032 0.038
30 20 0.030 0.050
30 30 0.030 0.046
30 40 0.033 0.040
40 20 0.035 0.052
40 30 0.033 0.047
40 40 0.033 0.039
n m MMAE for SM MMAE for SFEM
20 20 0.032 0.047
20 30 0.033 0.042
20 40 0.032 0.038
30 20 0.030 0.050
30 30 0.030 0.046
30 40 0.033 0.040
40 20 0.035 0.052
40 30 0.033 0.047
40 40 0.033 0.039
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