Fokker–Planck equations on homogeneous time scales: well-posedness, stability, and applications to stochastic processes

Authors

DOI:

https://doi.org/10.31489/2026m3/170-184

Keywords:

Fokker–Planck equations, homogeneous time scales, reflective boundary conditions, abstract Cauchy problem, backward Fokker–Planck operator, asymptotic stability, Ornstein–Uhlenbeck process, geometric Brownian motion

Abstract

We study one-dimensional Fokker–Planck equations on homogeneous time scales with spatially dependent drift and diffusion coefficients. The model is formulated under reflective boundary conditions and is transformed, through the stationary density, into an abstract Cauchy problem governed by the backward Fokker–Planck operator in a weighted Hilbert space. Using semigroup methods on time scales, we prove the existence and uniqueness of mild solutions and characterize when such solutions are classical. The relation between the forward Fokker–Planck operator and the backward operator is clarified through a conjugacy with the stationary profile, which also preserves the spectral structure. Under the stated assumptions, the backward operator is positive, self-adjoint, and has a discrete spectrum. These properties yield well-posedness of the time-scale Fokker–Planck problem and asymptotic stability of solutions toward the normalized stationary density. The homogeneous time-scale setting covers both continuous and discrete dynamics in a unified framework and allows spectral representations of the resulting evolution. The applicability of the theory is illustrated by the Ornstein–Uhlenbeck process and the geometric Brownian motion, showing how classical stochastic models can be represented within the proposed time-scale formulation.

References

  1. Pavliotis, G.A. (2014). Stochastic processes and applications. New York: Springer. https://doi.org/10.1007/978-1-4939-1323-7
  2. Breiten, T., Kunisch, K., & Pfeiffer, K. (2018). Control strategies for the Fokker–Planck equation. ESAIM: Control, Optimisation and Calculus of Variations, 24(2), 741–763. https://doi.org/10.1051/cocv/2017046
  3. Liberzon, D., & Brockett, R.W. (2000). Spectral analysis of Fokker–Planck and related operators arising from linear stochastic differential equations. SIAM Journal on Control and Optimization, 38(5), 1453–1467. https://doi.org/10.1137/S0363012998338193
  4. Heninger, J.M., Lippolis, D., & Cvitanovi´c, P. (2018). Perturbation theory for the Fokker–Planck operator in chaos. Communications in Nonlinear Science and Numerical Simulation, 55, 16–28. https://doi.org/10.1016/j.cnsns.2017.06.025
  5. Chandrasegaran, J., & Sharma, P. (2020). A short review on Fokker–Planck equations, entropy production and entropy generation. Bioinformatics Proteomics and Imaging Analysis, 4(1), 21– 24.
  6. Lucia, U. (2014). Entropy generation and the Fokker–Planck equation. Physica A: Statistical Mechanics and Its Applications, 393, 256–260. https://doi.org/10.1016/j.physa.2013.09.028
  7. Tom´e, T. (2006). Entropy production in nonequilibrium systems described by a Fokker–Planck equation. Brazilian Journal of Physics, 36(4A), 1285–1289. https://doi.org/10.1590/S0103-97332006000700029
  8. Flandoli, F., Leocata, M., & Ricci, C. (2021). The Navier–Stokes–Vlasov–Fokker–Planck system as a scaling limit of particles in a fluid. Journal of Mathematical Fluid Mechanics, 23, Article 40. https://doi.org/10.1007/s00021-021-00570-6
  9. Peeters, A.G., & Strintzi, D. (2008). The Fokker–Planck equation and its application in plasma physics. Annalen der Physik, 17(2–3), 142–157.
  10. Philipp, L., & Shizgal, B.D. (2019). A pseudospectral solution of a bistable Fokker–Planck equation that models protein folding. Physica A: Statistical Mechanics and Its Applications, 522, 158–166. https://doi.org/10.1016/j.physa.2019.01.146
  11. Chilarescu, C., & Vaneecloo, N. (2007). A stochastic approach to the Cobb–Douglas production function. Economics Bulletin, 3(9), 1–9.
  12. Brigo, D., & Mercurio, F. (2000). Option pricing impact of alternative continuous-time dynamics for discretely observed stock prices. Finance and Stochastics, 4(2), 147–159. https://doi.org/10.1007/s007800050009
  13. Bluman, G.W. (1971). Similarity solutions of the one-dimensional Fokker–Planck equation. International Journal of Non-Linear Mechanics, 6(2), 143–153. https://doi.org/10.1016/0020-7462(71)90051-5
  14. Risken, H. (1989). The Fokker–Planck equation: Method of solution and applications. Springer. https://doi.org/10.1007/978-3-642-61544-3
  15. Friedman, A. (1983). Partial Differential Equations of Parabolic Type. Malabar, FL: Robert E. Krieger Publishing Company.
  16. Largier, J.L. (2003). Considerations in estimating larval dispersal distances from oceanographic data. Ecological Applications, 13(1), 71–89. https://doi.org/10.1890/1051-0761(2003)013[0071:CIELDD]2.0.CO;2
  17. Sutrima, S., Mardiyana, M., Setiyowati, R., & Respatiwulan, R. (2021). A new approach on the well-posedness of nonautonomous Cauchy problems: An application in population growth. Journal of Nonlinear Functional Analysis, 2021, Article 29.
  18. Sutrima, S., Wibowo, S., & Setiyowati, R. (2024). Well-posedness of boundary control system of nonlinear chemical reaction. Nonlinear Dynamics and Systems Theory, 24(6), 648–662.
  19. Hilger, S. (1988). Ein Masskettenkalku¨l mit Anwendung auf Zentrumsmannigfaltigkeiten [A measure chain calculus with application to center manifolds] (dissertation). Universit¨at Wu¨rzburg [in German].
  20. Bohner, M., & Peterson, A. (2001). Dynamic equations on time scales: An introduction with applications. Birkh¨auser. https://doi.org/10.1007/978-1-4612-0201-1
  21. Bohner, M., & Peterson, A. (2003). Advances in dynamic equations on time scales. Birkh¨auser. https://doi.org/10.1007/978-0-8176-8230-9
  22. Bohner, M., & Peterson, A. (2001). First and second order linear dynamic equations on time scales. Journal of Difference Equations and Applications, 7(6), 767–792. https://doi.org/10.1080/10236190108808302
  23. Bohner, M. (2004). Calculus of variations on time scales. Dynamics of Continuous, Discrete and Impulsive Systems Series A: Mathematical Analysis, 13, 339–349.
  24. Bohner, M., Duque, C., Leiva, H., & Sivoli, Z. (2024). A lemma on C0-semigroups on the time scales and approximate controllability of the heat dynamic equation. Quaestiones Mathematicae, 47(9), 1807–1826. https://doi.org/10.2989/16073606.2024.2345845
  25. Bohner, M., Duque, C., & Leiva, H. (2022). Controllability of dynamic equations with memory. Nonlinear Dynamics and Systems Theory, 22(5), 489–502.
  26. Hamza, A.E., & Oraby, K.M. (2012). Stability of abstract dynamic equations on time scales. Advances in Difference Equations, 2012, Article 143. https://doi.org/10.1186/1687-1847-2012-143
  27. Hamza, A.E., & Oraby, K.M. (2015). Semigroups of operators and abstract dynamic equations on time scales. Applied Mathematics and Computation, 270, 334–348. https://doi.org/10.1016/j.amc.2015.07.110
  28. Henr´iquez, H.R., Lizama, C., & Mesquita, J.G. (2020). Semigroups on time scales and applications to abstract Cauchy problems. Topological Methods in Nonlinear Analysis, 56(1), 83–115. https://doi.org/10.12775/TMNA.2019.114
  29. Curtain, R., & Zwart, H. (1995). An introduction to infinite-dimensional linear system theory. Springer. https://doi.org/10.1007/978-1-4612-4224-6
  30. Montenegro, S., Moya, A.T., & Leiva, H. (2024). Quasi-semigroups of operators on homogeneous time scales. Nonautonomous Dynamical Systems, 11(1), Article 20240005. https://doi.org/10.1515/msds-2024-0005
  31. Cuchta, T., & Wintz, N. (2022). Periodic functions related to the Gompertz difference equation. Mathematical Biosciences and Engineering, 19(9), 8774–8785. https://doi.org/10.3934/mbe.2022407
  32. Cuchta, T., & Ferreira, R.A.C. (2023). The heat equation on time scales. Opuscula Mathematica, 43(4), 475–491. https://doi.org/10.7494/OpMath.2023.43.4.475
  33. P¨otzsche, C., Siegmund, S., & Wirth, F. (2003). A spectral characterization of exponential stability for linear time-invariant systems on time scales. Discrete and Continuous Dynamical Systems, 9(5), 1223–1241. https://doi.org/10.3934/dcds.2003.9.1223
  34. Gibson, A.G. (1972). A discrete Hille–Yosida–Phillips theorem. Journal of Mathematical Analysis and Applications, 39, 761–770. https://doi.org/10.1016/0022-247X(72)90196-5
  35. Sutrima, S., Siswanto, S., Wibowo, S., & Indrati, C.R. (2025). Fokker–Planck equation and its application in production function. Nonlinear Dynamics and Systems Theory, 25(6), 691–704.

Downloads

Published

30.09.2026

How to Cite

Sutrima, S., Wibowo, S., & Sutanto, S. (2026). Fokker–Planck equations on homogeneous time scales: well-posedness, stability, and applications to stochastic processes. Bulletin of the Karaganda University. Mathematics Series, 3(123), 170–184. https://doi.org/10.31489/2026m3/170-184

Issue

Section

MATHEMATICS