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Generative Modelling with Tensor Train approximations of Hamilton--Jacobi--Bellman equations
Sampling from probability densities is a common challenge in fields such as Uncertainty Quantification (UQ) and Generative Modelling (GM).
Approximating Langevin Monte Carlo with ResNet-like Neural Network architectures
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.
Decomposition of Industrial Systems for Energy Efficiency Optimization with OptTopo
The operation of industrial facilities is a broad field for optimization.
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