Ben Leimkuhler

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Martinsson A, Lu J, Leimkuhler B, et al. (2019) The simulated tempering method in the infinite switch limit with adaptive weight learning Journal of Statistical Mechanics: Theory and Experiment. 2019: 013207
Bethune I, Banisch R, Breitmoser E, et al. (2019) MIST: A simple and efficient molecular dynamics abstraction library for integrator development Computer Physics Communications. 236: 224-236
Fass J, Sivak DA, Crooks GE, et al. (2018) Quantifying Configuration-Sampling Error in Langevin Simulations of Complex Molecular Systems. Entropy (Basel, Switzerland). 20
Shang X, Kröger M, Leimkuhler B. (2018) Assessing numerical methods for molecular and particle simulation. Soft Matter. 13: 8565-8578
Leimkuhler B, Matthews C, Weare J. (2017) Ensemble preconditioning for Markov chain Monte Carlo simulation Statistics and Computing. 28: 277-290
Leimkuhler B, Matthews C. (2016) Efficient molecular dynamics using geodesic integration and solvent-solute splitting. Proceedings. Mathematical, Physical, and Engineering Sciences / the Royal Society. 472: 20160138
Leimkuhler B, Shang X. (2016) Adaptive thermostats for noisy gradient systems Siam Journal On Scientific Computing. 38: A712-A736
Leimkuhler B, Shang X. (2016) Pairwise adaptive thermostats for improved accuracy and stability in dissipative particle dynamics Journal of Computational Physics. 324: 174-193
Leimkuhler B, Shang X. (2015) On the numerical treatment of dissipative particle dynamics and related systems Journal of Computational Physics. 280: 72-95
Leimkuhler B, Matthews C, Tretyakov MV. (2014) On the long-time integration of stochastic gradient systems Proceedings of the Royal Society a: Mathematical, Physical and Engineering Sciences. 470