Model reduction methods for nuclear emulators

Abstract

The field of model order reduction (MOR) is growing in importance due to its ability to extract the key insights from complex simulations while discarding computationally burdensome and superfluous information. We provide an overview of MOR methods for the creation of fast & accurate emulators of memory- and compute-intensive nuclear systems. As an example, we describe how "eigenvector continuation" is a special case of a much more general and well-studied MOR formalism for parameterized systems. We continue with an introduction to the Ritz and Galerkin projection methods that underpin many such emulators, while pointing to the relevant MOR theory and its successful applications along the way. We believe that this will open the door to broader applications in nuclear physics and facilitate communication with practitioners in other fields.

13 pages. Added table and minor changes

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Cited by in corpus (26)

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• Training and Projecting: A Reduced Basis Method Emulator for Many-Body Physics
• Eigenvector continuation for emulating and extrapolating two-body resonances
• Emulating \emph{ab initio} computations of infinite nucleonic matter
• A Bayesian mixture model approach to quantifying the empirical nuclear saturation point
• Applications of reduced basis methods to the nuclear single particle spectrum
• Wave-function-based emulation for nucleon-nucleon scattering in momentum space
• Nuclear-matter saturation and symmetry energy within $\Delta$--full chiral effective field theory
• Building Surrogate Models of Nuclear Density Functional Theory with Gaussian Processesand Autoencoders
• Lattice Effective Field Theory Simulations of Nuclei
• Emulators for scarce and noisy data: application to auxiliary field diffusion Monte Carlo for the deuteron
• Reduced basis emulation of pairing in finite systems
• From chiral EFT to perturbative QCD: a Bayesian model mixing approach to symmetric nuclear matter
• Reduced basis surrogates for quantum spin systems based on tensor networks
• Floating block method for quantum Monte Carlo simulations
• Greedy Emulators for Nuclear Two-Body Scattering
• Trimmed Sampling Algorithm for the Noisy Generalized Eigenvalue Problem
• Perspectives on few-body cluster structures in exotic nuclei
• Posterior predictive distributions of neutron-deuteron cross sections
• Non-Hermitian Quantum Mechanics Approach for Extracting and Emulating Continuum Physics Based on Bound-State-Like Calculations
• IMSRG-Net: A machine learning-based solver for In-Medium Similarity Renormalization Group
• Non-Hermitian quantum mechanics approach for extracting and emulating continuum physics based on bound-state-like calculations: Detailed description
• Star Log-extended eMulation: a method for efficient computation of the Tolman-Oppenheimer-Volkoff equations
• Active learning emulators for nuclear two-body scattering in momentum space
• Constraining Hamiltonians from chiral effective field theory with neutron-star data
• Efficient emulation of nuclear ground states with neural-network variational Monte Carlo and eigenvector continuation