CORDIS Project
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This project focuses on improving numerical methods for solving partial differential equations in high-dimensional spaces. It aims to bridge the gap between theoretical complexity and practical implementation, particularly through low-rank tensor representations and neural networks, ensuring reliable results.
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 conjun…
RHEINISCH-WESTFAELISCHE TECHNISCHE HOCHSCHULE AACHEN
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