Themes 2019
HDA2019 topics comprise, but are not limited to, stochastic partial differential equations, data assimilation into PDEs, computational analysis of complex systems, computational uncertainty quantification, High-dimensional numerical integration and approximation.
Machine learning, Inverse Problems for PDEs, Tensor-Formats and Tensor approximation methods.
Main content
HDA2019 focuses on specific mathematical themes:
- sparsity quantification of input and output manifolds of many-parametric mathematical models,
- sparsity-exploiting numerical approximation techniques:
sparse grid, Smolyak algorithms, compressed sensing techniques, least-squares approximations. - Discrepancy and Dispersion theory.
- Quasi-Monte Carlo quadrature (lattice rules, polynomial lattice rules).
- Approximation and Computation of measure-valued solutions.
HDA2019 especially welcomes presentations on computational techniques for high-dimensional approximation in
- computational finance,
- computational biology,
- Deep Learning, Data Mining and Data Science