A robust collision source method for rank adaptive dynamical low-rank approximation in radiation therapy
Jonas Kusch, Pia Stammer

TL;DR
This paper introduces a robust low-rank approximation method with a collision source approach for high-dimensional radiation transport equations, improving computational efficiency and stability in radiation therapy modeling.
Contribution
It develops a novel collision source method combined with dynamical low-rank approximation for efficient, stable high-dimensional radiation transport simulations in therapy.
Findings
Method is L^2-stable under a non-dependent time step restriction.
Implicit scattering treatment avoids matrix inversions.
Numerical results demonstrate improved efficiency in radiation therapy models.
Abstract
Deterministic models for radiation transport describe the density of radiation particles moving through a background material. In radiation therapy applications, the phase space of this density is composed of energy, spatial position and direction of flight. The resulting six-dimensional phase space prohibits fine numerical discretizations, which are essential for the construction of accurate and reliable treatment plans. In this work, we tackle the high dimensional phase space through a dynamical low-rank approximation of the particle density. Dynamical low-rank approximation (DLRA) evolves the solution on a low-rank manifold in time. Interpreting the energy variable as a pseudo-time lets us employ the DLRA framework to represent the solution of the radiation transport equation on a low-rank manifold for every energy. Stiff scattering terms are treated through an efficient implicit…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSparse and Compressive Sensing Techniques · Numerical methods in inverse problems · Model Reduction and Neural Networks
