相关论文: Quasi-Random Lattice Model for Electrolyte Solutio…

Using quasiparticle models and imposing thermodynamic consistency, lattice data for the equation of state of deconfined QCD can be mapped to finite chemical potential. We consider a refinement of existing simple massive quasiparticle models…

We characterized the structural and dynamical properties of the quasi-liquid layer (QLL) at the surface of ice by molecular dynamics simulations with a thermodynamically consistent water model. Our simulations show that for three low-index…

Accurate models of alkali and halide ions in aqueous solution are necessary for computer simulations of a broad variety of systems. Previous efforts to develop ion force fields have generally focused on reproducing experimental measurements…

Reinforcement learning (RL) has emerged as a key approach for training agents in complex and uncertain environments. Incorporating statistical inference in RL algorithms is essential for understanding and managing uncertainty in model…

NJL-type effective models represent a low-energy realization of QCD and incorporate pertinent aspects such as chiral symmetry and its spontaneous breaking, the center symmetry in the heavy-quark limit as well as the axial anomaly. One such…

Quasi-Monte Carlo (QMC) integration of output functionals of solutions of the diffusion problem with a log-normal random coefficient is considered. The random coefficient is assumed to be given by an exponential of a Gaussian random field…

The quasicontinuum (QC) method, originally proposed by Tadmor, Ortiz and Phillips in 1996, is a computational technique that can efficiently handle regular atomistic lattices by combining continuum and atomistic approaches. In the present…

The quasi-nonlocal quasicontinuum method (QNL) is a consistent hybrid coupling method for atomistic and continuum models. Embedded atom models are empirical many-body potentials that are widely used for FCC metals such as copper and…

Quantum reinforcement learning (QRL) models augment classical reinforcement learning schemes with quantum-enhanced kernels. Different proposals on how to construct such models empirically show a promising performance. In particular, these…

Lattice QCD has the potential this decade to maximize the sensitivity of the entire flavor physics program to new physics and pave the way for understanding physics beyond the Standard Model at the LHC in the coming decade. However, the…

Chemical thermodynamic models of solvent and solute activities predict the equilibrium behaviour of aqueous solutions. How-ever, these models are semi-empirical. They represent micro-scale ion and solvent behaviours that control the…

This paper studies the quasi-maximum-likelihood estimator (QMLE) in a general conditionally heteroscedastic time series model of multiplicative form $X_t=\sigma_tZ_t$, where the unobservable volatility $\sigma_t$ is a parametric function of…

In this paper, we investigate quasi-maximum likelihood (QML) estimation for the parameters of a cointegrated solution of a continuous-time linear state space model observed at discrete time points. The class of cointegrated solutions of…

Quantile-based randomized Kaczmarz (QRK) was recently introduced to efficiently solve sparsely corrupted linear systems $\mathbf{A} \mathbf{x}^*+\mathbf{\epsilon} = \mathbf{b}$ [SIAM J. Matrix Anal. Appl., 43(2), 605-637], where…

Experimentally determined empirical formulae for the concentration dependent relative permittivity of aqueous solutions of MgCl$_{2}$ and NiCl$_{2}$ are utilized to calculate the osmotic coefficient and the mean activity coefficient of…

Using the representation of the quantum group $SL_q$(2) by the Weyl ope\-ra\-tors of the canonical commutation relations in quantum mechanics, we construct and solve a new vertex model on a square lattice. Random variables on horizontal…

Strong consistency and asymptotic normality of the Quasi-Maximum Likelihood Estimator (QMLE) are given for a general class of multidimensional causal processes. For particular cases already studied in the literature (for instance univariate…

In a previous paper (J. Comp. Phys. 230 (2011), 3668--3694), the authors proposed a new practical method for computing expected values of functionals of solutions for certain classes of elliptic partial differential equations with random…

The quasi-chemical organization of the potential distribution theorem -- molecular quasi-chemical theory (QCT) -- enables practical calculations and also provides a conceptual framework for molecular hydration phenomena. QCT can be viewed…

Quotient regularization models (QRMs) are a class of powerful regularization techniques that have gained considerable attention in recent years, due to their ability to handle complex and highly nonlinear data sets. However, the nonconvex…