Sparse grid approximations and fitting using generalised combination techniques

稀疏网格技术提供了一种有效的工具来处理 计算的维数灾难，这是一个不断的挑战， 对复杂数据进行建模。拟议的研究旨在 稀疏数据拟合算法的发展与分析 使用组合技术的变体的网格。的结果 研究是一种理论，它将提供适用性的见解， 的局限性和所提出的收敛性 算法这些结果将广泛适用于模拟 大规模和复杂的数据，如在 生物信息学、物理学和复杂系统的实验研究。

Sparse grid techniques provide an effective tool to deal with the computational curse of dimensionality which is a constant challenge in modelling complex data. The proposed research is aimed at the development and analysis of algorithms for data fitting with sparse grids using variants of the combination technique. The outcome of the research is a theory which will provide insights in the applicability, limitations and the convergence properties of the proposed algorithms. The outcomes will be widely applicable in modelling of large scale and complex data as is encountered in areas of bioinformatics, physics and experimental studies of complex systems.