Skip to main content

Advertisement

Log in

Jarzynski’s Equality, Fluctuation Theorems, and Variance Reduction: Mathematical Analysis and Numerical Algorithms

  • Published:
Journal of Statistical Physics Aims and scope Submit manuscript

Abstract

In this paper, we study Jarzynski’s equality and fluctuation theorems for diffusion processes. While some of the results considered in the current work are known in the (mainly physics) literature, we review and generalize these nonequilibrium theorems using mathematical arguments, therefore enabling further investigations in the mathematical community. On the numerical side, variance reduction approaches such as importance sampling method are studied in order to compute free energy differences based on Jarzynski’s equality.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Abrams, C., Bussi, G.: Enhanced sampling in molecular dynamics using metadynamics, replica-exchange, and temperature-acceleration. Entropy 16(1), 163–199 (2014)

    Article  MathSciNet  ADS  Google Scholar 

  2. Abreu, D., Seifert, U.: Extracting work from a single heat bath through feedback. EPL 94(1), 10001 (2011)

    Article  ADS  Google Scholar 

  3. Ambrosio, L., Gigli, N., Savaré, G.: Gradient Flows: In Metric Spaces and in the Space of Probability Measures. Lectures in Mathematics. Birkhäuser, Basel (2005)

    MATH  Google Scholar 

  4. Aurell, E., Mejía-Monasterio, C., Muratore-Ginanneschi, P.: Optimal protocols and optimal transport in stochastic thermodynamics. Phys. Rev. Lett. 106, 250601 (2011)

    Article  ADS  Google Scholar 

  5. Banyaga, A., Hurtubise, D.: Lectures on Morse Homology. Texts in the Mathematical Sciences. Springer, Berlin (2004)

    Book  MATH  Google Scholar 

  6. Bennett, C.H.: Efficient estimation of free energy differences from monte carlo data. J. Comput. Phys. 22(2), 245–268 (1976)

    Article  MathSciNet  ADS  Google Scholar 

  7. Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, New York (2006)

    MATH  Google Scholar 

  8. Callen, H.: Thermodynamics and an Introduction to Thermostatistics. Wiley, New York (1985)

    MATH  Google Scholar 

  9. Chaudhari, P., Oberman, A., Osher, S., Soatto, S., Carlier, G.: Deep relaxation: partial differential equations for optimizing deep neural networks. Submitted (2017)

  10. Chetrite, R., Gawȩdzki, K.: Fluctuation relations for diffusion processes. Commun. Math. Phys. 282(2), 469–518 (2008)

    Article  MathSciNet  MATH  ADS  Google Scholar 

  11. Christ, C.D., Mark, A.E., van Gunsteren, W.F.: Basic ingredients of free energy calculations: a review. J. Comput. Chem. 31(8), 1569–1582 (2010)

    Google Scholar 

  12. Ciccotti, G., Kapral, R., Vanden-Eijnden, E.: Blue moon sampling, vectorial reaction coordinates, and unbiased constrained dynamics. ChemPhysChem 6(9), 1809–1814 (2005)

    Article  Google Scholar 

  13. Ciccotti, G., Lelièvre, T., Vanden-Eijnden, E.: Projection of diffusions on submanifolds: application to mean force computation. Commun. Pure Appl. Math. 61(3), 371–408 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  14. Crooks, G.E.: Nonequilibrium measurements of free energy differences for microscopically reversible Markovian systems. J. Stat. Phys. 90(5), 1481–1487 (1998)

    Article  MathSciNet  MATH  ADS  Google Scholar 

  15. Crooks, G.E.: Entropy production fluctuation theorem and the nonequilibrium work relation for free energy differences. Phys. Rev. E 60, 2721–2726 (1999)

    Article  ADS  Google Scholar 

  16. Crooks, G.E.: Path-ensemble averages in systems driven far from equilibrium. Phys. Rev. E 61, 2361–2366 (2000)

    Article  ADS  Google Scholar 

  17. Darbon, J., Osher, S.: Algorithms for overcoming the curse of dimensionality for certain Hamilton–Jacobi equations arising in control theory and elsewhere. Res. Math. Sci. 3(1), 19 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  18. de Koning, M., Cai, W., Antonelli, A., Yip, S.: Efficient freeenergy calculations by the simulation of nonequilibrium processes. Comput. Sci. Eng. 2(3), 88–96 (2000)

    Article  Google Scholar 

  19. Dellago, C., Hummer, G.: Computing equilibrium free energies using non-equilibrium molecular dynamics. Entropy 16(1), 41 (2014)

    Article  MathSciNet  ADS  Google Scholar 

  20. Dupuis, P., Spiliopoulos, K., Wang, H.: Importance sampling for multiscale diffusions. Multiscale Model. Simul. 10(1), 1–27 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  21. Evans, L.C., Gariepy, R.F.: Measure Theory and Fine Properties of Functions. Studies in Advanced Mathematics. CRC Press, Boca Raton (1991)

    MATH  Google Scholar 

  22. Fleming, W.H., Soner, H.M.: Controlled Markov Processes and Viscosity Solutions. Springer, Berlin (2006)

    MATH  Google Scholar 

  23. Frenkel, D., Smit, B.: Understanding Molecular Simulation: From Algorithms to Applications. Computational Science Series. Elsevier, San Diego (2001)

    MATH  Google Scholar 

  24. Ge, H., Jiang, D.-Q.: Generalized Jarzynski’s equality of inhomogeneous multidimensional diffusion processes. J. Stat. Phys. 131(4), 675–689 (2008)

    Article  MathSciNet  MATH  ADS  Google Scholar 

  25. Givon, D., Kupferman, R., Stuart, A.: Extracting macroscopic dynamics: model problems and algorithms. Nonlinearity 17(6), R55–R127 (2004)

    Article  MathSciNet  MATH  ADS  Google Scholar 

  26. Gore, J., Ritort, F., Bustamante, C.: Bias and error in estimates of equilibrium free-energy differences from nonequilibrium measurements. Proc. Natl. Acad. Sci. USA 100(22), 12564–12569 (2003)

    Article  MathSciNet  MATH  ADS  Google Scholar 

  27. Gyöngy, I.: Mimicking the one-dimensional marginal distributions of processes having an Ito differential. Probab. Theory Relat. Fields 71(4), 501–516 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  28. Hartmann, C., Richter, L., Schütte, C., Zhang, W.: Variational characterization of free energy: theory and algorithms. Entropy 19(11), 626 (2017)

    Article  ADS  Google Scholar 

  29. Hartmann, C., Schütte, C., Weber, M., Zhang, W.: Importance sampling in path space for diffusion processes with slow-fast variables. Probab. Theory Relat. Fields 170, 177–228 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  30. Hartmann, C., Schütte, C., Zhang, W.: Model reduction algorithms for optimal control and importance sampling of diffusions. Nonlinearity 29(8), 2298–2326 (2016)

    Article  MathSciNet  MATH  ADS  Google Scholar 

  31. Haussmann, U.G., Pardoux, E.: Time reversal of diffusions. Ann. Probab. 14(4), 1188–1205 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  32. Hendrix, D.A., Jarzynski, C.: A “fast growth” method of computing free energy differences. J. Chem. Phys. 114(14), 5974–5981 (2001)

    Article  ADS  Google Scholar 

  33. Horowitz, J.M., Vaikuntanathan, S.: Nonequilibrium detailed fluctuation theorem for repeated discrete feedback. Phys. Rev. E 82, 061120 (2010)

    Article  ADS  Google Scholar 

  34. Hummer, G., Kevrekidis, I.G.: Coarse molecular dynamics of a peptide fragment: free energy, kinetics, and long-time dynamics computations. J. Chem. Phys. 118(23), 10762–10773 (2003)

    Article  ADS  Google Scholar 

  35. Hummer, G., Szabo, A.: Free energy reconstruction from nonequilibrium single-molecule pulling experiments. Proc. Natl. Acad. Sci. USA 98(7), 3658–3661 (2001)

    Article  ADS  Google Scholar 

  36. Jarzynski, C.: Equilibrium free-energy differences from nonequilibrium measurements: a master-equation approach. Phys. Rev. E 56, 5018–5035 (1997)

    Article  ADS  Google Scholar 

  37. Jarzynski, C.: Nonequilibrium equality for free energy differences. Phys. Rev. Lett. 78, 2690–2693 (1997)

    Article  ADS  Google Scholar 

  38. Jarzynski, C.: Rare events and the convergence of exponentially averaged work values. Phys. Rev. E 73, 046105 (2006)

    Article  ADS  Google Scholar 

  39. Jarzynski, C.: Nonequilibrium work relations: foundations and applications. Eur. Phys. J. B 64(3), 331–340 (2008)

    Article  MathSciNet  ADS  Google Scholar 

  40. Krantz, S.G., Parks, H.R.: Geometric Integration Theory. Birkhäuser, Boston (2008)

    Book  MATH  Google Scholar 

  41. Kubo, R.: The fluctuation-dissipation theorem. Rep. Prog. Phys. 29(1), 255 (1966)

    Article  MATH  ADS  Google Scholar 

  42. Legoll, F., Lelièvre, T.: Effective dynamics using conditional expectations. Nonlinearity 23(9), 2131–2163 (2010)

    Article  MathSciNet  MATH  ADS  Google Scholar 

  43. Lelièvre, T., Rousset, M., Stoltz, G.: Computation of free energy differences through nonequilibrium stochastic dynamics: the reaction coordinate case. J. Comput. Phys. 222(2), 624–643 (2007)

    Article  MathSciNet  MATH  ADS  Google Scholar 

  44. Lelièvre, T., Rousset, M., Stoltz, G.: Free Energy Computations: A Mathematical Perspective. N.J. Imperial College Press, London (2010)

    Book  MATH  Google Scholar 

  45. Lelièvre, T., Rousset, M., Stoltz, G.: Langevin dynamics with constraints and computation of free eneregy differences. Math. Comput. 81(280), 2071–2125 (2012)

    Article  MATH  Google Scholar 

  46. MacKay, D.J.C.: Information Theory, Inference, and Learning Algorithms. Cambridge University Press, New York (2002)

    Google Scholar 

  47. Maragliano, L., Vanden-Eijnden, E.: A temperature accelerated method for sampling free energy and determining reaction pathways in rare events simulations. Chem. Phys. Lett. 426(13), 168–175 (2006)

    Article  ADS  Google Scholar 

  48. Marconi, U.M.B., Puglisi, A., Rondoni, L., Vulpiani, A.: Fluctuationdissipation: response theory in statistical physics. Phys. Rep. 461(4), 111–195 (2008)

    Article  ADS  Google Scholar 

  49. Minh, D.D.L., Chodera, J.D.: Optimal estimators and asymptotic variances for nonequilibrium path-ensemble averages. J. Chem. Phys. 131(13), 134110 (2009)

    Article  ADS  Google Scholar 

  50. Oberhofer, H., Dellago, C.: Optimum bias for fast-switching free energy calculations. Comput. Phys. Commun. 179(13):41–45 (2008). Special Issue Based on the Conference on Computational Physics 2007CCP (2007)

  51. Oberhofer, H., Dellago, C., Geissler, P.L.: Biased sampling of nonequilibrium trajectories: can fast switching simulations outperform conventional free energy calculation methods? J. Phys. Chem. B 109(14), 6902–6915 (2005)

    Article  Google Scholar 

  52. Øksendal, B.: Stochastic Differential Equations: An Introduction with Applications, 5th edn. Springer, Berlin (2000)

    MATH  Google Scholar 

  53. Ponmurugan, M.: Generalized detailed fluctuation theorem under nonequilibrium feedback control. Phys. Rev. E 82, 031129 (2010)

    Article  ADS  Google Scholar 

  54. Rousset, M., Stoltz, G.: Equilibrium sampling from nonequilibrium dynamics. J. Stat. Phys. 123(6), 1251–1272 (2006)

    Article  MathSciNet  MATH  ADS  Google Scholar 

  55. Rubinstein, R.Y., Kroese, D.P.: The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning (Information Science and Statistics), 1st edn. Springer, Berlin (2004)

    Book  MATH  Google Scholar 

  56. Sagawa, T., Ueda, M.: Generalized Jarzynski equality under nonequilibrium feedback control. Phys. Rev. Lett. 104, 090602 (2010)

    Article  ADS  Google Scholar 

  57. Sagawa, T., Ueda, M.: Fluctuation theorem with information exchange: role of correlations in stochastic thermodynamics. Phys. Rev. Lett. 109, 180602 (2012)

    Article  ADS  Google Scholar 

  58. Sagawa, T., Ueda, M.: Nonequilibrium thermodynamics of feedback control. Phys. Rev. E 85, 021104 (2012)

    Article  ADS  Google Scholar 

  59. Schmiedl, T., Seifert, U.: Optimal finite-time processes in stochastic thermodynamics. Phys. Rev. Lett. 98, 108301 (2007)

    Article  ADS  Google Scholar 

  60. Spiliopoulos, K.: Large deviations and importance sampling for systems of slow-fast motion. Appl. Math. Optim. 67, 123–161 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  61. Then, H., Engel, A.: Computing the optimal protocol for finite-time processes in stochastic thermodynamics. Phys. Rev. E 77, 041105 (2008)

    Article  ADS  Google Scholar 

  62. Vaikuntanathan, S., Jarzynski, C.: Escorted free energy simulations: improving convergence by reducing dissipation. Phys. Rev. Lett. 100, 190601 (2008)

    Article  ADS  Google Scholar 

  63. Vaikuntanathan, S., Jarzynski, C.: Escorted free energy simulations. J. Chem. Phys. 134(5), 054107 (2011)

    Article  ADS  Google Scholar 

  64. Vanden-Eijnden, E.: Some recent techniques for free energy calculations. J. Comput. Chem. 30(11), 1737–1747 (2009)

    Article  Google Scholar 

  65. Vanden-Eijnden, E., Weare, J.: Rare event simulation of small noise diffusions. Commun. Pure Appl. Math. 65(12), 1770–1803 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  66. Weinan, E., Han, J., Jentzen, A.: Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations. Commun. Math. Stat. 5(4), 349–380 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  67. Ytreberg, F.M., Swendsen, R.H., Zuckerman, D.M.: Comparison of free energy methods for molecular systems. J. Chem. Phys. 125(18), 184114 (2006)

    Article  ADS  Google Scholar 

  68. Ytreberg, F.M., Zuckerman, D.M.: Single-ensemble nonequilibrium path-sampling estimates of free energy differences. J. Chem. Phys. 120(23), 10876–10879 (2004)

    Article  ADS  Google Scholar 

  69. Zhang, W.: Ergodic SDEs on submanifolds and related numerical sampling schemes. Submitted (2018)

  70. Zhang, W., Hartmann, C., Schütte, C.: Effective dynamics along given reaction coordinates, and reaction rate theory. Faraday Discuss. 195, 365–394 (2016)

    Article  ADS  Google Scholar 

  71. Zhang, W., Schütte, C.: Reliable approximation of long relaxation timescales in molecular dynamics. Entropy 19(7), 367 (2017)

    Article  ADS  Google Scholar 

  72. Zhang, W., Wang, H., Hartmann, C., Weber, M., Schütte, C.: Applications of the cross-entropy method to importance sampling and optimal control of diffusions. SIAM J. Sci. Comput. 36(6), A2654–A2672 (2014)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

The authors acknowledge financial support by the Einstein Center of Mathematics (ECMath) through project CH21, and the DFG-CRC 1114 “Scaling Cascades in Complex Systems” through project A05 “Probing scales in equilibrated systems by optimal nonequilibrium forcing” and B05 “Origin of the scaling cascades in protein dynamics”. Part of the work was done while CS and WZ were attending the program “Complex High-Dimensional Energy Landscapes” at IPAM (UCLA), 2017. The authors thank the institute for hospitality and support.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wei Zhang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix A: Connections with Thermodynamic Integration and Adiabatic Switching: Alchemical Transition Case

In this appendix, we study two (essentially equivalent) asymptotic regimes of nonequilibrium processes using formal arguments. In particular, we will derive the thermodynamic integration identity from Jarzynski’s identity, therefore bridging these two different free energy calculation methods. Let us point out that such a connection is indeed known in physics community [14], although we are not aware of its mathematical derivation in the literature. For simplicity, we only consider the alchemical transition case studied in Sect. 2 and assume the protocol \(\lambda (\cdot )\) is deterministic with \(\epsilon = 0\).

From Jarzynski’s equality to thermodynamic integration Thermodynamic integration is a well known method and has been widely used to compute free energy differences [23]. From the definition of the normalization constant \(Z(\cdot )\) in (10), we can derive the thermodynamic integration identity by the simple argument

$$\begin{aligned} \Delta F(T)&= F(\lambda (T)) - F(\lambda (0))\nonumber \\&= -\beta ^{-1} \ln \frac{Z(\lambda (T))}{Z(\lambda (0))} \nonumber \\&= -\beta ^{-1} \int _0^T \frac{d}{ds}\Big ( \ln \frac{Z(\lambda (s))}{Z(\lambda (0))}\Big )\, ds \nonumber \\&= \int _0^T \Big ( \frac{\int _{{\mathbb {R}}^n} e^{-\beta V(x,\lambda (s))} \nabla _\lambda V(x,\lambda (s))\, dx)}{Z(\lambda (s))}\Big )\,\cdot f(\lambda (s), s)\, ds \nonumber \\&= \int _0^T \big ({\mathbf {E}}_{\mu _{\lambda (s)}} (\nabla _{\lambda } V)\big ) \cdot f(\lambda (s), s)\, ds\,. \end{aligned}$$
(144)

In the following, using a formal argument, we show that the identity (144) corresponds to the Jarzynski’s equality (29) in certain asymptotic limit. For this purpose, we consider the dynamics

$$\begin{aligned} \begin{aligned} d x(s)&= \frac{1}{\tau } b(x(s), \lambda (s))\,ds + \sqrt{\frac{2\beta ^{-1}}{\tau }} \sigma (x(s), \lambda (s)) \,dw^{(1)}(s)\,, \end{aligned} \end{aligned}$$
(145)

on \(s \in [0, T]\), where \(0 < \tau \ll 1\) and \(\lambda (s)\) satisfies the ODE

$$\begin{aligned} {\dot{\lambda }}(s) = f(\lambda (s), s)\,. \end{aligned}$$
(146)

Clearly, dynamics (145) is related to (1) by rescaling time with the parameter \(0 < \tau \ll 1\), and its infinitesimal generator is \(\frac{1}{\tau } {\mathcal {L}}_1\), where \({\mathcal {L}}_1\) is defined in (6) with \(\lambda (\cdot )\) being time dependent. The main observation is that, repeating the argument from Sect. 2.2, the Jarzynski’s equality (29) holds for (145) and (146) for any \(\tau >0\). As a consequence,

$$\begin{aligned} e^{-\beta \Delta F(T)} = {\mathbf {E}}_{\mu (\lambda (0))} \Big (g(\cdot , \lambda (0), 0)\Big ) \,, \end{aligned}$$
(147)

where the function g now satisfies

$$\begin{aligned} \begin{aligned}&\partial _t g + \frac{1}{\tau }{\mathcal {L}}_{1} g + f\cdot \nabla _\lambda g - \beta \big (f\cdot \nabla _\lambda V \big ) g = 0\,, \quad 0 \le t < T\,,\\&g(\cdot ,\cdot , T) = 1\,. \end{aligned} \end{aligned}$$
(148)

To show that (147) reduces to the thermodynamic integration identity (144) as \(\tau \rightarrow 0\), it is enough to study the asymptotic limit of (148). To this end, we consider the formal asymptotic expansion

$$\begin{aligned} g = g_0 + \tau g_1+\tau ^2g_2 + \cdots \end{aligned}$$

as \(\tau \rightarrow 0\), where \(g_0, g_1, \cdots \) are functions independent of \(\tau \). Substituting this expansion into (148) and comparing terms of different powers of \(\tau \), we can conclude that \(g_0=g_0(\lambda , t)\) is independent of x and satisfies

$$\begin{aligned} \begin{aligned}&\partial _t g_0 + {\mathcal {L}}_1 g_1 + f\cdot \nabla _\lambda g_0 - \beta (f\cdot \nabla _\lambda V)g_0 = 0\,, \quad 0 \le t < T\\&g_0(\cdot , T) = 1\,. \end{aligned} \end{aligned}$$
(149)

Taking the expectation with respect to \(\mu _{\lambda }\) on both sides of (149) and noticing that \({\mathbf {E}}_{\mu _\lambda }({\mathcal {L}}_1 g_1) = 0\), we obtain

$$\begin{aligned} \begin{aligned}&\partial _t g_0 + f\cdot \nabla _\lambda g_0 - \beta \big (f \cdot {\mathbf {E}}_{\mu _{\lambda }}(\nabla _\lambda V)\big ) g_0 = 0\,, \quad 0 \le t < T\\&g_0(\cdot , T) = 1\,. \end{aligned} \end{aligned}$$
(150)

It is easy to verify that the solution of (150) is given by

$$\begin{aligned} g_0(\lambda , t) = e^{-\beta \int _t^T \big ({\mathbf {E}}_{\mu _{\lambda (s)}} (\nabla _{\lambda } V)\big ) \cdot f(\lambda (s), s)\, ds}\,, \end{aligned}$$
(151)

where \(\lambda (s)\) satisfies (146) with initial value \(\lambda (t) = \lambda \). Taking the limit \(\tau \rightarrow 0\) in (147) then yields

$$\begin{aligned} e^{-\beta \Delta F(T)} = \lim _{\tau \rightarrow 0} {\mathbf {E}}_{\mu (\lambda (0))} \Big (g(\cdot , \lambda (0), 0)\Big ) = g_0(\lambda (0), 0) = e^{-\beta \int _0^T \big ({\mathbf {E}}_{\mu _{\lambda (s)}} (\nabla _{\lambda } V)\big ) \cdot f(\lambda (s), s)\, ds}\,, \end{aligned}$$
(152)

which is equivalent to the thermodynamic integration identity (144).

Adiabatic switching Now we turn to another (equivalent) asymptotic regime where the protocol \(\lambda (\cdot )\) is switched infinitely slowly. Specifically, given \(\lambda _0, \lambda _1 \in {\mathbb {R}}^m\), the protocol \(\lambda (\cdot )\) satisfying \(\lambda (0) = \lambda _0\) and \(\lambda (T) = \lambda _1\) as \(T\rightarrow +\infty \) is called adiabatic switching. For the nonequilibrium process \(x(\cdot )\) in (1) under adiabatic switching, it is well known that we have

$$\begin{aligned} F(\lambda _1) - F(\lambda _0)&= \lim _{T\rightarrow +\infty } {\mathbf {E}}_{\lambda _0, 0} \big (W(T)\big ) \nonumber \\&= \lim _{T\rightarrow +\infty } {\mathbf {E}}_{\lambda _0, 0} \left( \int _0^T \nabla _\lambda V(x(s), \lambda (s)) \cdot f(\lambda (s), s)\, ds\right) \,, \end{aligned}$$
(153)

i.e., the free energy difference equals to the average work performed during the switching. In the following we provide a formal mathematical argument to derive the above identity. For this purpose, we define

$$\begin{aligned} u(x,\lambda , t) = {\mathbf {E}}\left( \int _t^T \nabla _\lambda V(x(s), \lambda (s)) \cdot f(\lambda (s), s)\, ds~\Big |~x(t) = x, \lambda (t) = \lambda \right) \,, \end{aligned}$$
(154)

which, by the Feynman–Kac formula, satisfies

$$\begin{aligned} \begin{aligned}&\partial _t u + {\mathcal {L}}_1 u + f\cdot \nabla _\lambda u + f\cdot \nabla _\lambda V = 0\,, \\&u(\cdot , \cdot , T) = 0\,. \end{aligned} \end{aligned}$$
(155)

Notice that, as \(T\rightarrow +\infty \), the switching becomes infinitely slow and \({\dot{\lambda }}(t) = f\) goes to zero. Instead, we rescale the time by \({\bar{t}} = \frac{t}{T} \in [0, 1]\) and define \({\bar{\lambda }}({\bar{t}}\,) = \lambda (\frac{{\bar{t}}}{\tau })\), where \(\tau = \frac{1}{T} \rightarrow 0\). \({\bar{\lambda }}(\cdot )\) satisfies \({\bar{\lambda }}(0) = \lambda _0, {\bar{\lambda }}(1) = \lambda _1\) and

$$\begin{aligned} \frac{d{\bar{\lambda }}}{d{\bar{t}}} = {\bar{f}}({\bar{\lambda }}({\bar{t}}\,), {\bar{t}}\,)\,, \end{aligned}$$
(156)

where \({\bar{f}}(\cdot , {\bar{t}}\,) = \frac{1}{\tau }f(\cdot , \frac{{\bar{t}}}{\tau })\) is a function of \({\mathcal {O}}(1)\). Under this time scaling, PDE (155) becomes

$$\begin{aligned} \begin{aligned}&\partial _{{\bar{t}}} u + \frac{1}{\tau } {\mathcal {L}}_1 u + {\bar{f}}\cdot \nabla _\lambda u + {\bar{f}}\cdot \nabla _\lambda V = 0\,, \quad 0 \le {\bar{t}} < 1\,,\\&u \equiv 0\,, \quad {\bar{t}} = 1\,. \end{aligned} \end{aligned}$$
(157)

Consider the expansion \(u = u_0 + \tau u_1 + \tau ^2 u_2 + \cdots \), then the same argument as above yields that the function \(u_0\) is independent of x and satisfies

$$\begin{aligned} \begin{aligned}&\partial _{{\bar{t}}} u_0 + {\bar{f}}\cdot \nabla _\lambda u_0 + {\bar{f}}\cdot {\mathbf {E}}_{\lambda } \big (\nabla _\lambda V\big ) = 0\,, \quad 0 \le {\bar{t}} < 1\,,\\&u_0 \equiv 0\,, \quad {\bar{t}} = 1\,. \end{aligned} \end{aligned}$$
(158)

The solution of (158) can be directly computed:

$$\begin{aligned} u_0(\lambda , {\bar{t}}\,) = \int _{{\bar{t}}}^1 {\mathbf {E}}_{{\bar{\lambda }}(s)} \big (\nabla _\lambda V\big )\cdot {\bar{f}}({\bar{\lambda }}(s), s)\,ds \,, \end{aligned}$$
(159)

where \({\bar{\lambda }}(\cdot )\) satisfies (156) on \([{\bar{t}}, 1]\) with \({\bar{\lambda }}({\bar{t}}\,) = \lambda \). In particular, taking \({\bar{t}} = 0\) and applying the thermodynamic integration identity (144), gives

$$\begin{aligned} u_0(\lambda _0, 0) = \int _{0}^1 {\mathbf {E}}_{{\bar{\lambda }}(s)} \big (\nabla _\lambda V\big )\cdot {\bar{f}}({\bar{\lambda }}(s), s)\,ds = F(\lambda _1) - F(\lambda _0)\,. \end{aligned}$$
(160)

Therefore,

$$\begin{aligned}&\lim _{T\rightarrow +\infty } {\mathbf {E}}_{\lambda _0, 0} \left( \int _0^T \nabla _\lambda V(x, \lambda (s)) \cdot f(\lambda (s), s)\, ds\right) \\&\quad = \lim _{\tau \rightarrow 0} {\mathbf {E}}_{\mu _{\lambda _0}} \big (u(\cdot , \lambda _0, 0)\big )\\&\quad = u_0(\lambda _0, 0) = F(\lambda _1) - F(\lambda _0)\,, \end{aligned}$$

which concludes the proof of (153).

Appendix B: Thermodynamic Integration Identity in the Reaction Coordinate Case

In the reaction coordinate case considered in Sect. 3, connections between the thermodynamic integration identity and the Jarzynski’s equality as well as the adiabatic switching regime can be studied using the same asymptotic argument as in Appendix A. In this section, we omit the derivation and only provide the thermodynamic integration identity. We emphasize that both the identity and its proof can be found in the literature, e.g., [42, 44]. The result is included for readers’ convenience.

Recall the definition of the probability measure \(\mu _z\) in (86), where the normalization constant is given by

$$\begin{aligned} Q(z) = \int _{{\mathbb {R}}^n} e^{-\beta V(y)} \delta \big (\xi (y) - z\big )\,dy \,,\quad z \in {\mathbb {R}}^d\,, \end{aligned}$$
(161)

and the free energy is defined in (87). Let \(z(s) \in {\mathbb {R}}^d\) satisfy the ODE (101) on [0, T]. Similar to the derivations in (144), and using Lemma 3 below, we can compute

$$\begin{aligned} \begin{aligned}&F(z(T)) - F(z(0)) \\&\quad = -\beta ^{-1} \ln \frac{Q(z(T))}{Q(z(0))}\\&\quad = -\beta ^{-1} \int _0^T \frac{d}{ds}\Big ( \ln \frac{Q(z(s))}{Q(z(0))}\Big )\, ds\\&\quad = -\beta ^{-1} \int _0^T \Big (\frac{1}{Q}\frac{\partial Q}{\partial z_{\gamma }}\Big )\big (z(s)\big )\,{\dot{z}}_{\gamma }(s)\, ds\\&\quad = \int _0^T {\mathbf {E}}_{\mu _{z(s)}}\Big [(a\nabla \xi _{\gamma '})_i (\Psi ^{-1})_{\gamma '\gamma } \frac{\partial V}{\partial y_i} - \frac{1}{\beta } \frac{\partial }{\partial y_i}\Big ( (a\nabla \xi _{\gamma '})_i (\Psi ^{-1})_{\gamma \gamma '}\Big )\Big ]\, {\dot{z}}_\gamma (s)\,ds\,, \end{aligned} \end{aligned}$$
(162)

where Einstein’s summation convention has been used.

Lemma 3

Let the function Q be defined in (161). For \(1 \le \gamma \le d\), we have

$$\begin{aligned} \frac{\partial Q}{\partial z_{\gamma }}(z) = -\beta Q(z) \int _{\Sigma _z} \Big [(a\nabla \xi _{\gamma '})_i (\Psi ^{-1})_{\gamma \gamma '} \frac{\partial V}{\partial y_i} - \frac{1}{\beta } \frac{\partial }{\partial y_i}\Big ( (a\nabla \xi _{\gamma '})_i (\Psi ^{-1})_{\gamma \gamma '}\Big )\Big ]\,\mu _z(dy)\,. \end{aligned}$$

Proof

Let \(\varphi : {\mathbb {R}}^d \rightarrow {\mathbb {R}}\) be a smooth test function with compact support. For \(1 \le \gamma \le d\), integrating by parts and using (161), we have

$$\begin{aligned} \int _{{\mathbb {R}}^d} \varphi (z) \frac{\partial Q}{\partial z_{\gamma }}(z) \, dz = -\int _{{\mathbb {R}}^d} \frac{\partial \varphi }{\partial z_{\gamma }}(z) Q(z)\, dz = -\int _{{\mathbb {R}}^n} \frac{\partial \varphi }{\partial z_{\gamma }}(\xi (y)) e^{-\beta V(y)}\, dy \,. \end{aligned}$$
(163)

On the other hand, from the relation

$$\begin{aligned} \frac{\partial \big (\varphi \circ \xi \big )}{\partial y_i}(y) = \frac{\partial \varphi }{\partial z_{\gamma '}}(\xi (y))\frac{\partial \xi _{\gamma '}}{\partial y_i}(y), \quad 1 \le i \le n\,, \end{aligned}$$

and the definition of the \(d \times d\) matrix \(\Psi \) in (88), we obtain

$$\begin{aligned} \frac{\partial \varphi }{\partial z_{\gamma }}(\xi (y)) = \Big [\frac{\partial \big (\varphi \circ \xi \big )}{\partial y_i} a_{ij} \frac{\partial \xi _{\gamma '}}{\partial y_j} (\Psi ^{-1})_{\gamma \gamma '}\,\Big ](y)\,. \end{aligned}$$
(164)

Therefore, integrating by parts, (163) simplifies to

$$\begin{aligned}&\int _{{\mathbb {R}}^d} \varphi (z) \frac{\partial Q}{\partial z_{\gamma }}(z) \, dz \\&\quad = \int _{{\mathbb {R}}^n} \varphi (\xi (y)) \frac{\partial }{\partial y_i}\Big (a_{ij} \frac{\partial \xi _{\gamma '}}{\partial y_j} (\Psi ^{-1})_{\gamma \gamma '} e^{-\beta V(y)}\Big )\, dy \, \\&\quad = \int _{{\mathbb {R}}^d} \varphi (z) \Big [ \int _{{\mathbb {R}}^n} \frac{\partial }{\partial y_i}\Big (a_{ij} \frac{\partial \xi _{\gamma '}}{\partial y_j} (\Psi ^{-1})_{\gamma \gamma '} e^{-\beta V(y)}\Big )\, \delta (\xi (y) - z)dy \Big ] dz\, , \end{aligned}$$

from which we can conclude after simplification. \(\square \)

Appendix C: An Alternative Proof of Theorem 2

In this appendix, we provide an alternative proof of Theorem 2. Different from the proof in Sect. 2.3 where only the Feynman–Kac formula has been used, the proof below relies on the combination of both the Feynman–Kac formula and Girsanov’s Theorem. While the idea is inspired by the derivations in [10], the proof below is shorter.

Alternative proof of Theorem 2

First of all, we recall the definition of u in (43) as well as the equations (40), (44), (45) used in the proof of Theorem 2 in Sect. 2.3. In accordance with (45), we define

$$\begin{aligned} \overline{{\mathcal {L}}} = \Big (J + a\nabla V + \frac{1}{\beta } \nabla \cdot a\Big ) \cdot \nabla + \frac{1}{\beta } a : \nabla ^2 + f \cdot \nabla _\lambda + \epsilon \,\alpha \alpha ^T:\nabla ^2_\lambda \,, \end{aligned}$$
(165)

and consider the function \(\omega (x, \lambda ,t) = u\big ( x, \lambda , T-t\,;x',\lambda ',t'\big )\). From (44) and (45), we know that \(\omega \) satisfies

$$\begin{aligned} \begin{aligned}&\frac{\partial \omega }{\partial t} + \overline{{\mathcal {L}}}_{(x,\lambda ,t)} \omega + \Big [\text{ div }(J + a\nabla V) + \text{ div }_\lambda \Big (f -\epsilon \nabla _\lambda \cdot (\alpha \alpha ^T)\Big ) + \eta \Big ]\omega = 0\,, \quad \forall t \in [0, T-t')\,,\\&\omega (x,\lambda ,t) = \delta (x'-x)\delta (\lambda '-\lambda )\,,\quad t = T-t'\,, \end{aligned}\nonumber \\ \end{aligned}$$
(166)

where \((x,\lambda ) \in {\mathbb {R}}^n \times {\mathbb {R}}^m\) and \(\overline{{\mathcal {L}}}_{(x,\lambda ,t)}\) is the operator (165) evaluated at \((x,\lambda ,t)\). On the other hand, applying the Feynman–Kac formula to (166), we observe that

$$\begin{aligned} \begin{aligned}&\omega (x, \lambda ,t) \\&\quad = \overline{{\mathbf {E}}}_{x,\lambda ,t}\bigg [\exp \bigg (\int _{t}^{T-t'} \Big (\text{ div }\big (J + a\nabla V\big ) + \text{ div }_\lambda \big (f -\epsilon \nabla _\lambda \cdot (\alpha \alpha ^T)\big ) + \eta \Big )\big ({\bar{x}}(s), {\bar{\lambda }}(s),s\big ) ds\bigg )\\&\qquad \times \delta \big ({\bar{x}}(T-t')-x'\big )\delta \big ({\bar{\lambda }}(T-t') -\lambda '\big )\bigg ]\,, \end{aligned} \end{aligned}$$
(167)

where \(\overline{{\mathbf {E}}}_{x,\lambda ,t}\) denotes the conditional expectation under the path ensemble of the dynamics

$$\begin{aligned} d {\bar{x}}(s)&= \Big (J+a\nabla V + \frac{1}{\beta }\nabla \cdot a\Big )\big ({\bar{x}}(s), {\bar{\lambda }}(s)\big )\, ds + \sqrt{2\beta ^{-1}} \sigma \big ({\bar{x}}(s), {\bar{\lambda }}(s)\big )\,dw^{(1)}(s) \end{aligned}$$
(168)

and the control protocol

$$\begin{aligned} d{\bar{\lambda }}(s)&= f({\bar{x}}(s), {\bar{\lambda }}(s), s)\, ds + \sqrt{2\epsilon }\, \alpha \big ({\bar{x}}(s), {\bar{\lambda }}(s),s\big ) dw^{(2)}(s)\,, \end{aligned}$$
(169)

starting from \({\bar{x}}(t) = x\) and \({\bar{\lambda }}(t) = \lambda \) at time t. Note that the infinitesimal generator of the dynamics (168) and (169) is given by the operator \(\overline{{\mathcal {L}}}\) in (165).

Now we apply Girsanov’s theorem to change the probability measure in (167) from the path ensemble of the dynamics (168), (169) to the path ensemble of the dynamics (15), (3). Specifically, starting from \((x, \lambda )\) at time t, let \({\mathbf {P}}_{x, \lambda }\) and \(\overline{{\mathbf {P}}}_{x,\lambda }\) denote the path measures on the time interval \([t,T-t']\) corresponding to (15), (3) and (168), (169), respectively. Applying Girsanov’s theorem, we obtain after some straightforward calculations

$$\begin{aligned} \begin{aligned} \frac{d{\mathbf {P}}_{x,\lambda }}{d \overline{{\mathbf {P}}}_{x,\lambda }} \big (x(\cdot ), \lambda (\cdot )\big )&= \exp \bigg [-\beta \int _{t}^{T-t'} \nabla V\big (x(s), \lambda (s)\big ) \cdot dx(s) \\&\quad + \beta \int _{t}^{T-t'} \Big (\nabla V \cdot \big (J + \frac{1}{\beta }\nabla \cdot a\big )\Big )\big (x(s),\lambda (s)\big )\, ds \bigg ]\,. \end{aligned} \end{aligned}$$
(170)

Therefore, changing the probability measure in (167) from \(\overline{{\mathbf {P}}}_{x,\lambda }\) to \({\mathbf {P}}_{x,\lambda }\), using (170), (13), we find

$$\begin{aligned}&u( x, \lambda ,T-t) = \omega ( x, \lambda ,t) \\&\quad = {\mathbf {E}}_{x,\lambda ,t}\bigg [\exp \bigg (\int _{t}^{T-t'} \Big (\text{ div }\big (J + a\nabla V\big ) + \text{ div }_\lambda \big (f -\epsilon \nabla _\lambda \cdot (\alpha \alpha ^T)\big ) + \eta \Big )\big (x(s), \lambda (s),s\big ) ds\bigg ) \\&\qquad \times \delta \big (x(T-t')-x'\big ) \delta \big (\lambda (T-t')-\lambda '\big ) \frac{d\overline{{\mathbf {P}}}_{x,\lambda }}{d{\mathbf {P}}_{x,\lambda }} \big (x(\cdot ), \lambda (\cdot )\big ) \bigg ] \\&\quad = {\mathbf {E}}_{x,\lambda ,t}\bigg [\exp \bigg ( \beta \int _{t}^{T-t'} \nabla V(x(s), \lambda (s)) \cdot dx(s) + \int _{t}^{T-t'} \big (a : \nabla ^2 V\big )\big (x(s), \lambda (s)\big ) ds\\&\qquad + \int _{t}^{T-t'} \Big (\text{ div }_\lambda \big (f -\epsilon \nabla _\lambda \cdot (\alpha \alpha ^T)\big ) + \eta \Big ) \big (x(s), \lambda (s),s\big ) ds\bigg ) \delta \big (x(T-t')-x'\big )\,\delta \big (\lambda (T-t')-\lambda '\big ) \bigg ]\\&\quad = {\mathbf {E}}_{x,\lambda ,t}\bigg [\exp \bigg ( \beta \int _{t}^{T-t'} \nabla V\big (x(s), \lambda (s)\big ) \circ dx(s)\\&\quad \quad + \int _{t}^{T-t'} \Big (\text{ div }_\lambda \big (f -\epsilon \nabla _\lambda \cdot (\alpha \alpha ^T)\big ) + \eta \Big ) \big (x(s), \lambda (s),s\big ) ds\bigg )\\&\qquad \times \delta \big (x(T-t')-x'\big )\,\delta \big (\lambda (T-t')-\lambda '\big ) \bigg ]\,. \end{aligned}$$

Note that in the last equality above, we have converted Ito integration to Stratonovich integration according to (15). Substituting t by \(T-t\), integrating by parts, and recalling the expression (43), we obtain

$$\begin{aligned} \begin{aligned}&e^{-\beta V(x',\lambda ')}\, {\mathbf {E}}^R_{x',\lambda ',t'}\bigg [ \exp \bigg (\int _{t'}^t \eta (x^R(s), \lambda ^R(s), T-s) ds\bigg ) \delta \big (x^R(t)-x\big )\delta \big (\lambda ^{R}(t)-\lambda \big )\bigg ]\\&\quad =e^{-\beta V(x,\lambda )}\,{\mathbf {E}}_{x,\lambda ,T-t}\\&\qquad \times \bigg [e^{-\beta {\mathcal {W}}} \exp \bigg (\int _{T-t}^{T-t'} \eta (x(s), \lambda (s), s) ds\bigg ) \delta \big (x(T-t')-x'\big )\delta \big (\lambda (T-t')-\lambda '\big ) \bigg ]\,, \end{aligned} \end{aligned}$$

where \({\mathcal {W}}\) is defined in (42). \(\square \)

Appendix D: Proof of Theorem 3

In this appendix, we provide the proof of Theorem 3 in Sect. 3.2.

Proof of Theorem 3

We consider the quantities on both sides of the equality (98). For the left hand side of (98), let us fix \((y',t') \in {\mathbb {R}}^n \times [0,T]\) and define the function u by

$$\begin{aligned} u\big (y,t\,;y',t'\big ) = {\mathbf {E}}_{y',t'}^R\bigg [ \exp \bigg (\int _{t'}^t \eta \big (y^R(s), T-s\big ) ds\bigg ) \delta \big (y^R(t)-y\big )\bigg ]\,, \end{aligned}$$
(171)

for \((y,t) \in {\mathbb {R}}^n \times [t',T]\). It is known that u satisfies the PDE

$$\begin{aligned} \begin{aligned}&\frac{\partial u}{\partial t} = \big ({\mathcal {L}}^R\big )^* u + \eta (y,T-t) \,u \,, \quad \forall ~(y, t) \in {\mathbb {R}}^n\times (t',T] \,,\\&u(y, t\,;y',t')=\delta (y-y')\,, \quad \text{ if }~~t=t'\,, \end{aligned} \end{aligned}$$
(172)

where the operator \({\mathcal {L}}^R\) is defined in (97) and \(\big ({\mathcal {L}}^R\big )^*\) denotes its formal \(L^2\) adjoint. A direct calculation shows that

$$\begin{aligned} \begin{aligned} \big ({\mathcal {L}}^R\big )^*\phi&= \bigg [\frac{\partial }{\partial y_i}\Big ((Pa)_{ij} \frac{\partial V}{\partial y_j}\Big )+ \frac{\partial }{\partial y_i} \Big ((\Psi ^{-1})_{\gamma \gamma '} (a\nabla \xi _\gamma )_i f^{-}_{\gamma '}\Big ) \bigg ]\phi \\&\quad + \bigg [ (Pa)_{ij} \frac{\partial V}{\partial y_j} + \frac{1}{\beta } \frac{\partial (Pa)_{ij}}{\partial y_j} + (\Psi ^{-1})_{\gamma \gamma '} (a\nabla \xi _\gamma )_i f^{-}_{\gamma '} \bigg ] \frac{\partial \phi }{\partial y_i} + \frac{1}{\beta } (Pa)_{ij} \frac{\partial ^2 \phi }{\partial y_i \partial y_j} \,, \end{aligned} \end{aligned}$$
(173)

for a smooth function \(\phi \).

For the right hand side of (98), fixing \((y', t') \in {\mathbb {R}}^n \times [0, T]\), we define the function g for \((y,t) \in {\mathbb {R}}^n \times [t',T]\) as

$$\begin{aligned} g(y,t) = {\mathbf {E}}_{y,T-t}\bigg [&e^{-\beta {\mathcal {W}}} \exp \bigg (\int _{T-t}^{T-t'} \eta \big (y(s), s\big ) ds\bigg ) \delta \big (y(T-t')-y'\big ) \bigg ]\,, \end{aligned}$$

where \({\mathcal {W}}\) is defined in (99), and the dynamics \(y(\cdot )\) satisfies the SDE (93). Using the same argument as in Lemma 1, we can verify that g satisfies the PDE

$$\begin{aligned} \begin{aligned}&\frac{\partial g}{\partial t} = \overline{{\mathcal {L}}}\, g + \eta (\cdot , T-t) g \,,\qquad \forall \, (y,t) \in {\mathbb {R}}^n \times (t',T] \,,\\&g(y,t) = \delta (y-y') \,, \qquad \text{ if }~~t=t'\,, \end{aligned} \end{aligned}$$
(174)

where the operator \(\overline{{\mathcal {L}}}\) is defined as

$$\begin{aligned} \begin{aligned} \overline{{\mathcal {L}}}\,\phi&= \bigg [-\beta (\Psi ^{-1})_{\gamma \gamma '} (a\nabla \xi _\gamma )_i f^-_{\gamma '} \frac{\partial V}{\partial y_i} + \frac{\partial }{\partial y_i} \Big ((\Psi ^{-1})_{\gamma \gamma '} (a\nabla \xi _\gamma )_i f^-_{\gamma '}\Big )\bigg ] \phi \\&\quad + {\mathcal {L}}^{\perp } \phi + (\Psi ^{-1})_{\gamma \gamma '} (a\nabla \xi _\gamma )_if^-_{\gamma '} \frac{\partial \phi }{\partial y_i} \end{aligned} \end{aligned}$$
(175)

for a smooth function \(\phi \). Now consider the function \(\omega (y,t) = e^{-\beta V(y)} g(y,t)\). A direct calculation shows that

$$\begin{aligned} \begin{aligned} e^{-\beta V} {\mathcal {L}}^{\perp } g&= e^{-\beta V} \bigg [-(Pa)_{ij} \frac{\partial V}{\partial y_j}\frac{\partial \big (e^{\beta V}\omega \big )}{\partial y_i} + \frac{1}{\beta } \frac{\partial (Pa)_{ij}}{\partial y_j} \frac{\partial \big (e^{\beta V}\omega \big )}{\partial y_i} + \frac{1}{\beta } (Pa)_{ij} \frac{\partial ^2 \big (e^{\beta V}\omega \big )}{\partial y_i\partial y_j}\bigg ]\,\\&= \bigg [\frac{\partial }{\partial y_i}\Big ((Pa)_{ij} \frac{\partial V}{\partial y_j}\Big )\bigg ]\omega + \bigg [(Pa)_{ij} \frac{\partial V}{\partial y_j} + \frac{1}{\beta } \frac{\partial (Pa)_{ij}}{\partial y_j}\bigg ] \frac{\partial \omega }{\partial y_i} + \frac{1}{\beta } (Pa)_{ij} \frac{\partial ^2 \omega }{\partial y_i\partial y_j}\,, \\ e^{-\beta V} \frac{\partial g}{\partial y_i}&= e^{-\beta V} \frac{\partial \big (e^{\beta V} \omega \big )}{\partial y_i} = \beta \frac{\partial V}{\partial y_i}\omega + \frac{\partial \omega }{\partial y_i} \,. \end{aligned} \end{aligned}$$
(176)

Combining (97), (174), (175), (176), it follows that the function \(\omega \) satisfies the PDE

$$\begin{aligned}&\frac{\partial \omega }{\partial t} = e^{-\beta V} \Big [\overline{{\mathcal {L}}}\,g + \eta (\cdot , T-t) g\Big ]= \big ({\mathcal {L}}^R\big )^* \,\omega + \eta (y,T-t)\,\omega \,,\quad \forall \,(y, t) \in {\mathbb {R}}^n \times (t',T] \,,\\&\omega (y,t) = e^{-\beta V(y')} \delta (y-y')\,,\quad \text{ if }~~ t=t'\,. \end{aligned}$$

Comparing this with the equation of function u in (172), we obtain

$$\begin{aligned} e^{-\beta V(y')} u(y,t\,;y',t') = \omega (y,t), \end{aligned}$$

which is equivalent to (98). \(\square \)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hartmann, C., Schütte, C. & Zhang, W. Jarzynski’s Equality, Fluctuation Theorems, and Variance Reduction: Mathematical Analysis and Numerical Algorithms. J Stat Phys 175, 1214–1261 (2019). https://doi.org/10.1007/s10955-019-02286-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10955-019-02286-4

Keywords

Navigation