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pro vyhledávání: '"Schütte, Janina"'
Our method proposes the efficient generation of samples from an unnormalized Boltzmann density by solving the underlying continuity equation in the low-rank tensor train (TT) format. It is based on the annealing path commonly used in MCMC literature,
Externí odkaz:
http://arxiv.org/abs/2412.07637
Autor:
Schütte, Janina Enrica, Eigel, Martin
A neural network architecture is presented that exploits the multilevel properties of high-dimensional parameter-dependent partial differential equations, enabling an efficient approximation of parameter-to-solution maps, rivaling best-in-class metho
Externí odkaz:
http://arxiv.org/abs/2408.10838
Autor:
Schütte, Janina E., Eigel, Martin
To solve high-dimensional parameter-dependent partial differential equations (pPDEs), a neural network architecture is presented. It is constructed to map parameters of the model data to corresponding finite element solutions. To improve training eff
Externí odkaz:
http://arxiv.org/abs/2403.12650
We sample from a given target distribution by constructing a neural network which maps samples from a simple reference, e.g. the standard normal distribution, to samples from the target. To that end, we propose using a neural network architecture ins
Externí odkaz:
http://arxiv.org/abs/2311.03242