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Proving DRACO will deliver
The United States is now closer than it has been in over five decades to launching the first nuclear thermal rocket into space, thanks to DRACO—the Demonstration Rocket for Agile Cislunar Orbit.
Thomas E. Booth
Nuclear Science and Engineering | Volume 138 | Number 1 | May 2001 | Pages 96-103
Technical Paper | doi.org/10.13182/NSE01-A2204
Articles are hosted by Taylor and Francis Online.
It is well known that zero-variance Monte Carlo solutions are possible if an exact importance function is available to bias the random walks. Geometric convergence with iteration has been demonstrated when the importance function estimated on the n'th iteration is used to bias the random walks on the n + 1st iteration, i.e., adaptive importance sampling. Note that geometric convergence with iteration may be less efficient than a nonadaptive Monte Carlo calculation if the time per iteration grows too fast. This paper shows a general method for sampling the zero-variance kernels enabling a Monte Carlo solution that converges inversely with the computer time.