Normalizing Flows for Microscopic Many-Body Calculations: An Application to the Nuclear Equation of State
We report that normalizing flows are a class of machine learning models used to construct a complex distribution through a bijective mapping of a simple base distribution. We demonstrate that normalizing flows are particularly well suited as a Monte Carlo integration framework for quantum many-body calculations that require the repeated evaluation of high-dimensional integrals across smoothly varying integrands and integration regions. As an example, we consider the finite-temperature nuclear equation of state. An important advantage of normalizing flows is the ability to build highly expressive models of the target integrand, which we demonstrate enables precise evaluations of the nuclear free energy and its derivatives. Furthermore, we show that a normalizing flow model trained on one target integrand can be used to efficiently calculate related integrals when the temperature, density, or nuclear force is varied. This work will support future efforts to build microscopic equations of state for numerical simulations of supernovae and neutron star mergers that employ state-of-the-art nuclear forces and many-body methods.
- Research Organization:
- Texas A & M Univ., College Station, TX (United States)
- Sponsoring Organization:
- National Science Foundation (NSF); USDOE National Nuclear Security Administration (NNSA)
- Grant/Contract Number:
- NA0003841
- OSTI ID:
- 1850367
- Journal Information:
- Physical Review Letters, Journal Name: Physical Review Letters Journal Issue: 6 Vol. 127; ISSN 0031-9007
- Publisher:
- American Physical Society (APS)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
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