Machine learning extrapolation frameworks improve precision and uncertainty estimates for ab initio nuclear calculations of energies, radii, and electromagnetic observables by learning convergence patterns from truncated model spaces.
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High-precision ab initio nuclear theory: Learning to overcome model-space limitations
Machine learning extrapolation frameworks improve precision and uncertainty estimates for ab initio nuclear calculations of energies, radii, and electromagnetic observables by learning convergence patterns from truncated model spaces.