Gast-Prof. Dr. Tim Brereton

Research Interests

Rare event simulation.

Stochastic geometry.

Multiscale methods.

Probabilistic modelling in physics. 

Submitted Papers

Hirsch, C., Brereton, T. and Schmidt, V. Percolation and convergence properties of graphs related to minimal spanning forests. (submitted).

Koubek, A., Pawlas, Z., Brereton, T., Kriesche, B. and Schmidt, V. Testing the random field model hypothesis for random marked closed sets. (submitted).

Sedivy, O., Brereton, T., Westhoff, D., Polivka, L., Benes, V., Schmidt, V. and Jaeger, A. 3D reconstruction of grains in polycrystalline materials using a tessellation model with curved grain boundaries. (submitted).

Publications

Brereton, T. and Schmidt, V. Stochastic models for charge transport in disordered media. Forthcoming.

Spettl, A., Brereton, T., Duan, Q., Werz, T., Krill, C. E., Kroese, D. P. and Schmidt, V. Fitting Laguerre tessellation approximations to tomographic image data. Philosopical Magazine. Forthcoming.

Brereton, T., Eckard, C. and Schmidt, V. Estimating hitting probabilities of an interacting particle system on a graph (2015). Proceedings of the 21st International Congress on Modelling and Simulation. Forthcoming.

Feinauer, J., Brereton, T., Spettl, A., Weber, M., Manke, I. and Schmidt, V. Stochastic 3D modeling of the microstructure of lithium-ion battery anodes via Gaussian random fields on the sphere (2015). Computational Materials Science. 106: 137 - 146.

Roland, M., Kruglova, A., Gaiselmann, G., Brereton, T., Schmidt, V., Muecklich, F. and Diebels, S. (2015). Numerical simulation and comparison of a real Al-Si alloy with virtually generated alloys. Archive of Applied Mechanics. 85: 1161 - 1171.

Westhoff, D., van Franeker, JJ., Brereton, T., Kroese, D.P., Janssen, R.A.J. and Schmidt, V. (2015). Stochastic modeling and predictive simulations for the microstructure of organic semiconductor films processed with different spin coating velocities. Modelling and Simulation in Materials Science. 23: 045003.

Brereton, T., Hirsch, C., Schmidt, V. and Kroese, D.P. (2014). A critical exponent for shortest-path scaling in continuum percolation. Journal of Physics A. 47 (50).

Kroese D.P., Brereton T., Taimre T. and Botev, Z.I. (2014). Why the Monte Carlo Method is so important today. Wiley Interdisciplinary Reviews: Computational Statistics. DOI: 10.1002/wics.1314.

Duan Q., Kroese D.P., Brereton T., Spettl A. and Schmidt V. (2014). Inverting Laguerre Tessellations. The Computer Journal. DOI: 10.1093/comjnl/bxu029.

Stenzel, O., Hirsch, C., Brereton T., Baumeier, B., Andrienko, D., Kroese, D.P. and Schmidt, V. (2014). A general framework for consistent estimation of charge transport properties via random walks in random environments. SIAM: Multiscale Modeling and Simulation. 12 (3), 1108 - 1134.

Brereton, T., Stenzel, O., Baumeier B., Andrienko, D., Schmidt, V., Kroese D. P. (2014). Efficient Simulation of Markov Chains using Segmentation. Methodology and Computing in Applied Probability. 16: 465 - 484.

Brereton, T., Chan J.C.C. and Kroese, D.P. (2013). Monte Carlo Methods for Portfolio Credit Risk. In: Credit Portfolio Securitizations and Derivatives, H. Scheule and D. Rosch (Eds.), John Wiley & Sons, New York, pp 128 - 151.

Brereton, T., Kroese, D.P., Stenzel, O., Schmidt, V. and Baumeier, B. (2012). Efficient simulation of charge transport in deep-trap media, Proceedings of the 2012 Winter Simulation Conference, C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, and A. M. Uhrmacher, eds.

Brereton, T, Chan, J.C.C, and Kroese, D.P. (2011). Fitting mixture importance sampling distributions via improved cross-entropy. Proceedings of the 2011 Winter Simulation Conference. S. Jain, R. R. Creasey, J. Himmelspach, K.P. White, and M. Fu, eds., pp. 422 -428.

Working Papers

Brereton, T. and Kroese, D. P. Automatic importance sampling with mixtures. Working paper (under preparation).

Brereton, T, Smith, A. and Schmidt, V. Efficient estimation of charge mobility for high carrier densities. Working paper (under preparation).

Smith, A., Brereton, T., and Schmidt, V. Efficiency of importance samplers that learn. Working paper (under preparation).