ERC Consolidator Grant · 2024
Computational Complexity of Highly Nonlinear Approximations
The efficient numerical treatment of partial differential equations on high-dimensional spaces often requires approximation methods involving a high degree of nonlinearity, such as low-rank tensor representations or neural networks. By exploiting structural features of solutions, such approaches in many cases promise extremely efficient approximations. However, due to the corresponding greater difficulty of computing highly compressed representations, such results need to be considered in conjunction with the costs of numerical methods for constructing these approximations. A main objective of the project is to address the gap between theoretical complexity bounds and the performance of…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.