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Department of Applied Mathematics and Theoretical Physics

Publications

Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format
PB Rohrbach, S Dolgov, L Grasedyck, R Scheichl
– SIAM/ASA Journal on Uncertainty Quantification
(2022)
10,
1191
Multilevel simulation of hard-sphere mixtures
PB Rohrbach, H Kobayashi, R Scheichl, NB Wilding, RL Jack
– The Journal of chemical physics
(2022)
157,
124109
Convergence of random-weight sequential Monte Carlo methods
PB Rohrbach, RL Jack
(2022)
Multilevel simulation of hard-sphere mixtures
PB Rohrbach, H Kobayashi, R Scheichl, NB Wilding, RL Jack
(2022)
Critical point for demixing of binary hard spheres.
H Kobayashi, PB Rohrbach, R Scheichl, NB Wilding, RL Jack
– Phys Rev E
(2021)
104,
044603
Efficient Bayesian inference of fully stochastic epidemiological models with applications to COVID-19.
YI Li, G Turk, PB Rohrbach, P Pietzonka, J Kappler, R Singh, J Dolezal, T Ekeh, L Kikuchi, JD Peterson, A Bolitho, H Kobayashi, ME Cates, R Adhikari, RL Jack
– Royal Society open science
(2021)
8,
211065
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format
PB Rohrbach, S Dolgov, L Grasedyck, R Scheichl
(2020)
Correction of coarse-graining errors by a two-level method: Application to the Asakura-Oosawa model.
H Kobayashi, PB Rohrbach, R Scheichl, NB Wilding, RL Jack
– J Chem Phys
(2019)
151,
144108

Research Group

Soft Matter

Room

G2.06

Telephone

01223 760454