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The adaptive moment estimation (ADAM) method effectively optimizes stochastic k-eigenvalue nuclear systems. Low-compute time, high-variance gradient estimates improve optimization performance in nuclear system modeling.

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Area of Science:

  • Nuclear Engineering
  • Computational Science
  • Optimization Methods

Background:

  • Gradient descent methods are valuable for nuclear system optimization.
  • Stochasticity in k-eigenvalue gradients presents computational challenges.
  • Existing methods struggle with the inherent uncertainty in nuclear system calculations.

Purpose of the Study:

  • To evaluate the suitability of the ADAM optimizer for k-eigenvalue nuclear systems.
  • To assess ADAM's performance in handling stochastic gradients.
  • To determine the impact of gradient estimation properties on optimization success.

Main Methods:

  • Utilized challenge problems specifically designed for k-eigenvalue nuclear system optimization.
  • Applied the ADAM (adaptive moment estimation) gradient descent algorithm.
  • Investigated the effect of varying gradient estimate properties (compute time, variance).

Main Results:

  • ADAM successfully optimized k-eigenvalue nuclear systems, managing stochastic gradients.
  • The method demonstrated robustness despite the inherent uncertainty in eigenvalue problems.
  • Lower computational time and higher variance in gradient estimates correlated with improved optimization performance.

Conclusions:

  • ADAM is a viable and effective tool for optimizing stochastic k-eigenvalue nuclear systems.
  • The findings suggest that strategic gradient estimation can enhance optimization efficiency.
  • This research provides a pathway for more computationally efficient nuclear system analysis.