First NQS variational Monte Carlo calculation of excited states in A=4 nuclei and hypernuclei, reproducing benchmarks and providing the first ab initio M1 transition strength for ^{4}_ΛH consistent with weak-coupling limit at 1.3% suppression.
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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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Neural-network excited states of $A=4$ nuclei and hypernuclei
First NQS variational Monte Carlo calculation of excited states in A=4 nuclei and hypernuclei, reproducing benchmarks and providing the first ab initio M1 transition strength for ^{4}_ΛH consistent with weak-coupling limit at 1.3% suppression.
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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.