However, Monte Carlo sampling can become extremely computationally expensive. In this paper, we develop two strategies to reduce the computational cost of the ...
In this paper, we develop two strategies to reduce the computational cost of the algorithm. ... Qiao, “Path Planning Algorithm Based on Sub-. Region for ...
However, Monte Carlo sampling can become extremely computationally expensive. In this paper, we develop two strategies to reduce the computational cost of the ...
Two strategies to reduce the computational cost of the Path Distribution Planner, one of which performs lazy sampling within the planning algorithm itself ...
Oct 13, 2013 · However, Monte Carlo sampling can become extremely computationally expensive. In this paper, we develop two strategies to reduce the ...
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Feb 27, 2021 · Reduce simulated space through symmetry or reducing dimension. · Lower the cost of each simulated iteration via caching or precomputation.
Nov 13, 2010 · Results across multiple calibration case studies demonstrate actual preemption computational savings ranging from 14% to 49%, 34% to 59%, and 52 ...
This paper introduces a Monte-Carlo algorithm for online planning in large. POMDPs. The algorithm combines a Monte-Carlo update of the agent's.
The Monaco treatment planning system combines Monte Carlo dose calculation accuracy with robust optimization tools to provide high-quality radiotherapy ...
We have developed a real-time, deep learning (DL)-based dose denoiser that can be plugged into a current GPU-based MC dose engine to enable real-time MC dose ...