The workshop will include talks with extensive discussions and poster sessions. We invite participants to submit draft papers describing the technical content of proposed contribution. We especially encourage submissions that directly address any of the topics, but related papers are also welcomed. The selected submission may be accepted either as an oral presentation or as a poster presentation.
Submission of papers due:
Notification of acceptance for the workshop and decision on selection of contributions for the book due:
July 19, 2013 SENT
Bartels S., Kružík M.: An efficient approach to the numerical solution of rate-independent problems with nonconvex energies. In: Multiscale Model. Simul., vol. 9, No. 3, pp. 1276-1300, 2011
Brzeźniak, Z., Ondreját, M.: Weak Solutions to Stochastic Wave Equations with Values in Riemannian Manifolds. In: Communications in Partial Differetial Equations, 36: 1624-1653, 2011
(v abecedním pořadí podle prvního autora)
V. Peterka, J. Krýže, and A. Fořtová. Numerical solution of Wiener-Hopf equation in statistical identification of linear dynamic systems. Kybernetika, 2:331-346, 1966. Download.
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One of the key objectives of any rolling mill control system is to keep the thickness of the processed material within the prescribed tolerance band, which can be as low as +-10 micrometers for thin strips. Failure to comply with the tolerances results in losses which, according to experts estimate, might go up to 10% of the profit for poorly equipped rolling mills. Unfortunately, no practical direct measurement of the gauge within the rolling gap is possible. Strip thickness can be measured 50--100cm after the rolling gap with a high transport delay (20--120 samples).
A state space model is frequently used for a description of real systems. Usually, some state variables are hidden and cannot be measured directly and some model parameters are unknown. Then, the need for learning, i.e., the state filtering and parameter estimation, arises. Probabilistic models provide a suitable description of the always uncertain reality and call for such approaches as Bayesian learning. Uncertainties are standardly modelled by the Gaussian distribution. This leads to Kalman-filter-based algorithms.