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Rare events in stochastic systems

  1. Statistical analysis of the first passage path ensemble of jump processes (with M. von Kleist and C. Schütte), J. Stat. Phys., 170(4), pp. 809-843, 2018. doi arxiv
  2. Nucleation Rate Calculation for the Phase Transition of Diblock Copolymers under Stochastic Cahn-Hilliard Dynamics (with T. Li and P. Zhang), Multiscale Model. Simul., 11(1), pp.385-409, 2013.   doi pdf
  3. Numerical study for the nucleation of one-dimensional stochastic Cahn-Hilliard dynamics (with T. Li and P. Zhang), Commun. Math. Sci, 10(4), pp.1105-1132, 2012.   doi pdf

Optimal control, importance sampling of stochastic processes

  1. Importance sampling in path space for diffusion processes with slow-fast variables (with C. Hartmann, C. Schütte and M. Weber), Probab. Theory Related Fields, 170(1), pp.177-228, 2018.   doi arxiv
  2. Optimal control of Markov jump processes : asymptotic analysis, algorithms, and application to modelling of chemical reaction systems (with C. Hartmann and M. von Kleist), Commun. Math. Sci., Vol.16, No.2, pp. 293-331, 2018.   doi arxiv
  3. Variational characterization of free energy: Theory and algorithms (with C. Hartmann, L. Richter, C. Schütte), Entropy 2017, 19(11), 626. doi
  4. Model reduction algorithms for optimal control and importance sampling of diffusions (with C. Hartmann and C. Schütte), Nonlinearity Vol.29, No.8, pp.2298-2326, 2016.   doi
  5. Applications of the cross-entropy method to importance sampling and optimal control of diffusions (with H. Wang, C. Hartmann, M. Weber and C. Schütte), Siam. J. Sci. Comput. Vol.36, No.6, pp. A2654-A2672, 2014.  doi pdf
  6. Optimal control of multiscale systems using reduced-order models (with C. Hartmann, J.C. Latorre and G.A. Pavliotis), J. Comput. Dyn. Vol.1, No.2, pp.279-306, 2014.   doi

Model reduction of diffusion processes

  1. On finding optimal collective variables for complex systems by minimizing the deviation between effective and full dynamics (with C. Schütte), 2024. arxiv
  2. Pathwise estimates for effective dynamics: the case of nonlinear vectorial reaction coordinates (with T. Lelièvre), Multiscale Model. Simul., 17(3), 1019-1051, 2019. doi arxiv
  3. Reliable approximation of long relaxation timescales in molecular dynamics (with C. Schütte), Entropy 2017, 19(7), 367. doi
  4. Effective dynamics along given reaction coordinates, and reaction rate theory (with C. Hartmann and C. Schütte), Faraday Discuss., 2016, Vol.195, pp.365-394. doi

Monte Carlo sampling methods on submanifolds

  1. Multiple projection MCMC algorithms on submanifolds (with T. Lelièvre and G. Stoltz), IMA J. Numer. Anal. 43(2), pp. 737-788, 2023.   doi arxiv code
  2. Non-reversible sampling schemes on submanifolds (with U. Sharma), Siam J. Numer. Anal., 59(6), 2989-3031, 2021.   doi arxiv
  3. Ergodic SDEs on submanifolds and related numerical sampling schemes, ESAIM:M2AN, 54(2), pp. 391-430, 2020.   doi arxiv code

Fluctuation relations for non-equilibrium processes

  1. Some new results on relative entropy production, time reversal, and optimal control of time-inhomogeneous diffusion processes, J. Math. Phys. 62(4), 043302, 2021.   doi arxiv
  2. Jarzynski equality, fluctuation theorem, and variance reduction: Mathematical analysis and numerical algorithms (with C. Hartmann and C. Schütte), J. Stat. Phys., 175(6), pp. 1214-1261, 2019. doi arxiv


  1. Analyzing multimodal probability measures with autoencoders (with T. Lelièvre and T. Pigeon and G. Stoltz), J. Phys. Chem. B, Vol.128, No.11, pp.2607-2631, 2024. doi arxiv
  2. EPR-Net: Constructing non-equilibrium potential landscape via a variational force projection formulation (with Y. Zhao and T. Li), Natl. Sci. Rev. Vol.11, Issue 7, 2024. doi arxiv code
  3. Understanding recent deep-learning techniques for identifying collective variables of molecular dynamics (with C. Schütte), Proc. Appl. Math. Mech., 23, e202300189, 2023. doi (extended version on arxiv)
  4. Solving eigenvalue PDEs of metastable diffusion processes using artificial neural networks (with T. Li and C. Schütte), J. Comput. Phys., 465, 111377, 2022.   doi arxiv
  5. Learning chemical reaction networks from trajectory data (with S. Klus, T. Conrad, C. Schütte), Siam J. Appl. Dyn. Syst., 18(4), pp. 2000-2046, 2019.   doi arxiv code