A5075360156- Quantum, superfluid, helium dynamics
- Pulsars and Gravitational Waves Research
- Cold Atom Physics and Bose-Einstein Condensates
- Quantum many-body systems
- Atomic and Subatomic Physics Research
- Quantum Chromodynamics and Particle Interactions
- High-Energy Particle Collisions Research
- Particle physics theoretical and experimental studies
- Spectroscopy and Quantum Chemical Studies
- Geophysics and Sensor Technology
- Neural Networks and Applications
- Physics of Superconductivity and Magnetism
- Neural Networks and Reservoir Computing
- Quantum and electron transport phenomena
- High-pressure geophysics and materials
- Advanced Thermodynamics and Statistical Mechanics
- Nuclear physics research studies
- Advanced Chemical Physics Studies
Michigan State University
2017-2024
Facility for Rare Isotope Beams
2023
National Superconducting Cyclotron Laboratory
2017-2018
Abstract Ultra-cold Fermi gases exhibit a rich array of quantum mechanical properties, including the transition from fermionic superfluid Bardeen-Cooper-Schrieffer (BCS) state to bosonic Bose-Einstein condensate (BEC). While these properties can be precisely probed experimentally, accurately describing them poses significant theoretical challenges due strong pairing correlations and non-perturbative nature particle interactions. In this work, we introduce Pfaffian-Jastrow neural-network...
Low-density neutron matter is characterized by fascinating emergent quantum phenomena, such as the formation of Cooper pairs and onset superfluidity. We model this density regime capitalizing on expressivity hidden-nucleon neural-network states combined with variational Monte Carlo stochastic reconfiguration techniques. Our approach competitive auxiliary-field diffusion method at a fraction computational cost. Using leading-order pionless effective field theory Hamiltonian, we compute energy...
Ultra-cold Fermi gases display diverse quantum mechanical properties, including the transition from a fermionic superfluid BCS state to bosonic BEC state, which can be probed experimentally with high precision. However, theoretical description of these properties is challenging due onset strong pairing correlations and non-perturbative nature interaction among constituent particles. This work introduces novel Pfaffian-Jastrow neural-network that includes backflow transformation based on...
Charge fluctuations for a baryon-neutral quark-gluon plasma have been calculated in lattice gauge theory. These provide well-posed rigorous representation of the quark chemistry vacuum temperatures above ${T}_{c}\ensuremath{\gtrsim}155\phantom{\rule{0.28em}{0ex}}\mathrm{MeV}$. Due to finite lifetime and spatial extent fireball created relativistic heavy ion collisions, charge-charge correlations can only equilibrate small volumes due time required transport charge. This constraint leads...
We introduce a message-passing-neural-network-based wave function Ansatz to simulate extended, strongly interacting fermions in continuous space. Symmetry constraints, such as translation symmetries, can be readily embedded the model. demonstrate its accuracy by simulating ground state of homogeneous electron gas three spatial dimensions at different densities and system sizes. With orders magnitude fewer parameters than state-of-the-art neural-network functions, we better or comparable...
Quark-Gluon plasmas produced in relativistic heavy-ion collisions quickly expand and cool, entering a phase consisting of multiple interacting hadronic resonances just below the QCD deconfinement temperature, $T\sim 155$ MeV. Numerical microscopic simulations have emerged as principal method for modeling behavior stage collisions, but transport properties that characterize these are not well understood. Methods presented here extracting shear viscosity, two parameters emerge Israel-Stewart...
In this study, we explore the similarities and differences between variational Monte Carlo techniques that employ conventional artificial neural network representations of ground-state wave function for fermionic systems. Our primary focus is on shallow architectures, specifically restricted Boltzmann machine, examine unsupervised learning algorithms are appropriate modeling complex many-body correlations. We assess advantages drawbacks functions by applying them to a range circular quantum...
An accurate description of low-density nuclear matter is crucial for explaining the physics neutron star crusts. In density range between approximately 0.01 fm$^{-3}$ and 0.1 fm$^{-3}$, transitions from neutron-rich nuclei to various higher-density pasta shapes, before ultimately reaching a uniform liquid. this work, we introduce variational Monte Carlo method based on neural Pfaffian-Jastrow quantum state, which allows us model transition liquid phase microscopically. At low densities,...
Low-density neutron matter is characterized by fascinating emergent quantum phenomena, such as the formation of Cooper pairs and onset superfluidity. We model this density regime capitalizing on expressivity hidden-nucleon neural-network states combined with variational Monte Carlo stochastic reconfiguration techniques. Our approach competitive auxiliary-field diffusion method at a fraction computational cost. Using leading-order pionless effective field theory Hamiltonian, we compute energy...
We discuss differences and similarities between variational Monte Carlo approaches that use conventional artificial neural network parameterizations of the ground-state wave function for systems fermions. focus on a relatively shallow neural-network architectures, so called restricted Boltzmann machine, unsupervised learning algorithms are suitable to model complicated many-body correlations. analyze strengths weaknesses functions by solving various circular quantum-dots systems. Results up...