Molecular Dynamics in a Grand Ensemble: Bergmann-Lebowitz model and Adaptive Resolution Simulation

This article deals with the molecular dynamics simulation of open systems that can exchange energy and matter with a reservoir; the physics of the reservoir and its interactions with the system are described by the model introduced by Bergmann and Lebowitz.Despite its conceptual appeal, the model did not gain popularity in the field of molecular simulation and, as a consequence, did not play a role in the development of open system molecular simulation techniques, even though it can provide the conceptual legitimation of simulation techniques that mimic open systems. We shall demonstrate that the model can serve as a tool to devise both numerical procedures and conceptual definitions of physical quantities that cannot be defined in a straightforward way by systems with a fixed number of molecules. In particular, we discuss the utility of the Bergmann-Lebowitz (BL) model for the calculation of equilibrium time correlation functions within the Grand Canonical Adaptive Resolution method (GC-AdResS) and report numerical results for the case of liquid water.Comment: 31 pages, 6 figure

Is there a third order phase transition for supercritical fluids?

We prove that according to Molecular Dynamics (MD) simulations of liquid mixtures of Lennard-Jones (L-J) particles, there is no third order phase transition in the supercritical regime beyond Andrew's critical point. This result is in open contrast with recent theoretical studies and experiments which instead suggest not only its existence but also its universality regarding the chemical nature of the fluid. We argue that our results are solid enough to go beyond the limitations of MD and the generic character of L-J models, thus suggesting a rather smooth liquid-vapor thermodynamic behavior of fluids in supercritical regime.Comment: 13 pages, 6 figure

Identification of second-order kernels in aerodynamics

Volterra series is one of the powerful system identification methods for representing the nonlinear dynamic system behavior. The methods of step response and impulse response are commonly applied to a discrete aerodynamic Computational Fluid Dynamic (CFD) to identify the first- and second-order Volterra kernels. A critical problem, however, is the difficulty of identifying the second-order Volterra kernels correctly in CFD-based method. In this paper the second-order Volterra kernel function is expanded in terms of Chebyshev functions to reduce the size of the problem and the accuracy of the identification is also improved based on a third-order reduced model of Volterra series

DOI 10.1007/s10463-007-0158-9

Local influence analysis for penalized Gaussian likelihood estimation in partially linear single-index model

Optimization of the Turbulence Model on Numerical Simulations of Flow Field within a Hydrocyclone

Reynolds Stress Model and Large Eddy Simulation are used to respectively perform numerical simulation for the flow field of a hydrocyclone. The three-dimensional hexahedral computational grids were generated. Turbulence intensity, vorticity, and the velocity distribution of different cross sections were gained. The velocity simulation results were compared with the LDV test results, and the results indicated that Large Eddy Simulation was more close to LDV experimental data. Large Eddy Simulation was a relatively appropriate method for simulation of flow field within a hydrocyclone

Estimating Effects of Long-Term Treatments

Estimating the effects of long-term treatments in A/B testing presents a significant challenge. Such treatments -- including updates to product functions, user interface designs, and recommendation algorithms -- are intended to remain in the system for a long period after their launches. On the other hand, given the constraints of conducting long-term experiments, practitioners often rely on short-term experimental results to make product launch decisions. It remains an open question how to accurately estimate the effects of long-term treatments using short-term experimental data. To address this question, we introduce a longitudinal surrogate framework. We show that, under standard assumptions, the effects of long-term treatments can be decomposed into a series of functions, which depend on the user attributes, the short-term intermediate metrics, and the treatment assignments. We describe the identification assumptions, the estimation strategies, and the inference technique under this framework. Empirically, we show that our approach outperforms existing solutions by leveraging two real-world experiments, each involving millions of users on WeChat, one of the world's largest social networking platforms

Bergmann–Lebowitz model and adaptive resolution simulation

This article deals with the molecular dynamics simulation of open systems that can exchange energy and matter with a reservoir; the physics of the reservoir and its interactions with the system are described by the model introduced by Bergmann and Lebowitz (P G Bergmann and J L Lebowitz 1955 Phys. Rev. 99 578). Despite its conceptual appeal, the model did not gain popularity in the field of molecular simulation and, as a consequence, did not play a role in the development of open system molecular simulation techniques, even though it can provide the conceptual legitimation of simulation techniques that mimic open systems. We shall demonstrate that the model can serve as a tool in devising both numerical procedures and conceptual definitions of physical quantities that cannot be defined in a straightforward way by systems with a fixed number of molecules. In particular, we discuss the utility of the Bergmann–Lebowitz (BL) model for the calculation of equilibrium time correlation functions within the grand canonical adaptive resolution method (GC-AdResS) and report numerical results for the case of liquid water

Inductive Graph Transformer for Delivery Time Estimation

Providing accurate estimated time of package delivery on users' purchasing pages for e-commerce platforms is of great importance to their purchasing decisions and post-purchase experiences. Although this problem shares some common issues with the conventional estimated time of arrival (ETA), it is more challenging with the following aspects: 1) Inductive inference. Models are required to predict ETA for orders with unseen retailers and addresses; 2) High-order interaction of order semantic information. Apart from the spatio-temporal features, the estimated time also varies greatly with other factors, such as the packaging efficiency of retailers, as well as the high-order interaction of these factors. In this paper, we propose an inductive graph transformer (IGT) that leverages raw feature information and structural graph data to estimate package delivery time. Different from previous graph transformer architectures, IGT adopts a decoupled pipeline and trains transformer as a regression function that can capture the multiplex information from both raw feature and dense embeddings encoded by a graph neural network (GNN). In addition, we further simplify the GNN structure by removing its non-linear activation and the learnable linear transformation matrix. The reduced parameter search space and linear information propagation in the simplified GNN enable the IGT to be applied in large-scale industrial scenarios. Experiments on real-world logistics datasets show that our proposed model can significantly outperform the state-of-the-art methods on estimation of delivery time. The source code is available at: https://github.com/enoche/IGT-WSDM23.Comment: 9 pages, accepted to WSDM 202