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Found 3 papers, 3 papers with code
From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs
The numerical approximation of partial differential equations (PDEs) poses formidable challenges in high dimensions since classical grid-based methods suffer from the so-called curse of dimensionality.
Pricing high-dimensional Bermudan options with hierarchical tensor formats
An efficient compression technique based on hierarchical tensors for popular option pricing methods is presented.
Solving high-dimensional parabolic PDEs using the tensor train format
High-dimensional partial differential equations (PDEs) are ubiquitous in economics, science and engineering.
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