Simulation Step Research Articles

Scattering wave packets of hadrons in gauge theories: Preparation on a quantum computer

Quantum simulation holds promise of enabling a complete description of high-energy scattering processes rooted in gauge theories of the Standard Model. A first step in such simulations is preparation of interacting hadronic wave packets. To create the wave packets, one typically resorts to adiabatic evolution to bridge between wave packets in the free theory and those in the interacting theory, rendering the simulation resource intensive. In this work, we construct a wave-packet creation operator directly in the interacting theory to circumvent adiabatic evolution, taking advantage of resource-efficient schemes for ground-state preparation, such as variational quantum eigensolvers. By means of an ansatz for bound mesonic excitations in confining gauge theories, which is subsequently optimized using classical or quantum methods, we show that interacting mesonic wave packets can be created efficiently and accurately using digital quantum algorithms that we develop. Specifically, we obtain high-fidelity mesonic wave packets in the Z2 and U(1) lattice gauge theories coupled to fermionic matter in 1+1 dimensions. Our method is applicable to both perturbative and non-perturbative regimes of couplings. The wave-packet creation circuit for the case of the Z2 lattice gauge theory is built and implemented on the Quantinuum H1-1 trapped-ion quantum computer using 13 qubits and up to 308 entangling gates. The fidelities agree well with classical benchmark calculations after employing a simple symmetry-based noise-mitigation technique. This work serves as a step toward quantum computing scattering processes in quantum chromodynamics.

Predicting arbitrary state properties from single Hamiltonian quench dynamics

Analog quantum simulation is an essential routine for quantum computing and plays a crucial role in studying quantum many-body physics. Typically, the quantum evolution of an analog simulator is largely determined by its physical characteristics, lacking the precise control or versatility of quantum gates. This limitation poses challenges in extracting physical properties on analog quantum simulators, an essential step of quantum simulations. To address this issue, we introduce the protocol, which uses a single quench Hamiltonian for estimating arbitrary state properties, eliminating the need for ancillary systems and random unitaries. Additionally, we derive the sample complexity of this protocol and show that it performs comparably to the classical shadow protocol. The Hamiltonian shadow protocol does not require sophisticated control and can be applied to a wide range of analog quantum simulators. We demonstrate its utility through numerical demonstrations with Rydberg atom arrays under realistic parameter settings. The new protocol significantly broadens the application of randomized measurements for analog quantum simulators without precise control and ancillary systems. Published by the American Physical Society 2024

Data-driven homogenisation of viscoelastic porous elastomers: feedforward versus knowledge-based neural networks

A computational framework is established to implement time-dependent data-driven surrogate constitutive models for the homogenised mechanical response of porous elastomers at large strains. The aim is to enhance the computational efficiency of multiscale analyses through the use of these surrogate models. To achieve this, explicit finite element (FE) simulations are conducted to predict the homogenised response of a cubic unit cell of a porous elastomer, using two different viscoelastic descriptions of the parent material, subject to pseudo-random, multiaxial, non-proportional histories of macroscopic strains. The histories of homogenised variables extracted from each set of FE predictions form a training dataset, which is used to train two different surrogate models, both relying on artificial neural networks (NNs). The first model predicts the increment in macroscopic stress over a simulation step, as a function of the macroscopic stress and strain at the beginning of the step, of the prescribed macroscopic strain increment, and of the corresponding time increment. The second model uses the same inputs and outputs but tests a knowledge-based modelling approach: it relies on the aid of an additional nonlinear elastic constitutive model, which is time- and path-independent and known a priori. This model represents an existing base of knowledge which is augmented and corrected by an NN after training on viscoelastic data. The data-driven surrogate model, therefore, learns the viscoelastic behaviour of the unit cell starting from knowledge of its elastic response. The two surrogate models are found to have comparable and very high accuracies at capturing the homogenised unit cell response to highly complex loading cases across a range of viscoelastic properties. Hyperparameter optimisation shows that the second, knowledge-based model requires a simpler NN and therefore incurs a smaller computational cost.

LIC-CGAN: fast lithography latent images calculation method for large-area masks using deep learning

Latent image calculation for large-area masks is an indispensable but time-consuming step in lithography simulation. This paper presents LIC-CGAN, a fast method for three-dimensional (3D) latent image calculation of large-area masks using deep learning. Initially, the library of mask clips and their corresponding latent images is established, which is then used to train conditional generative adversarial networks (CGANs). The large area layout is divided into mask clips based on local pattern features. If a mask clip matches one from the training library, its latent image can be obtained directly. Otherwise, the CGANs are employed to calculate its local latent image. Finally, all local latent images are synthesized to simulate the entire latent image. The proposed method is applied to lithography simulations for display panels, demonstrating high accuracy and a speed-up of 2.5 to 4.7 times compared to the rigorous process.

Parameter Estimation of Uncertain Differential Equations Driven by Threshold Ornstein–Uhlenbeck Process with Application to U.S. Treasury Rate Analysis

Uncertain differential equations, as an alternative to stochastic differential equations, have proved to be extremely powerful across various fields, especially in finance theory. The issue of parameter estimation for uncertain differential equations is the key step in mathematical modeling and simulation, which is very difficult, especially when the corresponding terms are driven by some complicated uncertain processes. In this paper, we propose the uncertainty counterpart of the threshold Ornstein–Uhlenbeck process in probability, named the uncertain threshold Ornstein–Uhlenbeck process, filling the gaps of the corresponding research in uncertainty theory. We then explore the parameter estimation problem under different scenarios, including cases where certain parameters are known in advance while others remain unknown. Numerical examples are provided to illustrate our method proposed. We also apply the method to study the term structure of the U.S. Treasury rates over a specific period, which can be modeled by the uncertain threshold Ornstein–Uhlenbeck process mentioned in this paper. The paper concludes with brief remarks and possible future directions.

Downwind and upwind approximations for primal and dual problems of elasto-plasticity with Prandtl–Reuss type material laws

Modern adaptive finite element (FE) algorithms for solution of initial-boundary-value-problems (IBVP) employ goal-oriented error measures in order to assess the quality of computational results for the physical event under investigation. However, traditional time-stepping algorithms for solution of the corresponding dual problem run backwards-in-time, which due to additional storage requirements might become a serious drawback when an extensive number of time steps for the FEM simulation arises. In this paper, we take advantage of an end-boundary-value-problem (EBVP) associated to IBVP, with corresponding dual-problem running forwards in time. In order to obtain a unified framework for numerical approximation of primal and dual weak forms for both, IBVP and EBVP, respectively, we apply the concept of downwind and upwind approximations not only to the trial functions but also to the test functions. This results into eight different integration schemes. On this basis, as a main result of this contribution, a time-stepping algorithm is obtained, which runs forwards-in-time for the dual problem and therefore avoids the additional storage requirements of the traditional backwards-in-time stepping procedures. The presented algorithm is numerically tested and validated for a CT-specimen for elastic and elasto-plastic behavior, where the constitutive equations are written in Prandtl–Reuss type format.

Development and comparison of model-integrated evidence approaches for bioequivalence studies with pharmacokinetic end points.

By applying nonlinear mixed-effect (NLME) models, model-integrated evidence (MIE) approaches are able to analyze bioequivalence (BE) data with pharmacokinetic end points that have sparse sampling, which is problematic for non-compartmental analysis (NCA). However, MIE approaches may suffer from inflation of type I error due to underestimation of parameter uncertainty and to the assumption of asymptotic normality. In this study, we developed a MIE BE analysis method that is based on a pre-defined model and consists of several steps including model fitting, uncertainty assessment, simulation, and BE determination. The presented MIE approach has several improvements compared with the previously reported model-integrated methods: (1) treatment, sequence, and period effects are only added to absorption parameters (such as relative bioavailability and rate of absorption) instead of all PK parameters; (2) a simulation step is performed to generate confidence intervals of the pharmacokinetic metrics for BE assessment; and (3) in an effort to maintain type I error, two more advanced parameter uncertainty evaluation approaches are explored, a nonparametric (case resampling) bootstrap, and sampling importance resampling (SIR). To evaluate the developed method and compare the uncertainty assessment methods, simulation experiments were performed for BE studies using a two-way crossover design with different amounts of information (sparse to rich designs) and levels of variability. Based on the simulation results, the method using SIR for parameter uncertainty quantification controls type I error at the nominal level of 0.05 (i.e., the significance level set for BE evaluation) even for studies with small sample size and/or sparse sampling. As expected, our MIE approach for BE assessment exhibited higher power than the NCA-based method, especially as the data becomes sparser and/or more variable.

Early-stage design support for ice-shell architecture with integrated snow drift simulation tool

The data on snow distribution on roofs can aid in optimizing the weatherability of ice-shell architecture. However, there is currently a lack of technology for predicting snow drift in early-stage design. The study proposes a snow drift simulation method for ice shells, utilizing Butterfly to call the OpenFOAM solver. A snow drift simulation tool based on this method was developed and validated through three case studies. The validation results provide suggestions for mesh accuracy and time steps for typical ice-shell architecture simulation with the tool. Simulation results from the tool are used to analyze the impact of ice-shell architectural specific features (texture, plane, orientation) on weatherability, focusing on the coupling optimization of snow and radiation for typical ice shells. The study demonstrates the tool's effectiveness, simplifying the simulation process and extending its applicability to other cold-region buildings. The feature analysis indicates that the ribbed texture more effectively blocks solar radiation through snow distribution.

Research Progress in Spatiotemporal Dynamic Simulation of LUCC

Land Use and Land Cover Change (LUCC) represents the interaction between human societies and the natural environment. Studies of LUCC simulation allow for the analysis of Land Use and Land Cover (LULC) patterns in a given region. Moreover, these studies enable the simulation of complex future LUCC scenarios by integrating multiple factors. Such studies can provide effective means for optimizing and making decisions about the future patterns of a region. This review conducted a literature search on geographic models and simulations in the Web of Science database. From the literature, we summarized the basic steps of spatiotemporal dynamic simulation of LUCC. The focus was on the current major models, analyzing their characteristics and limitations, and discussing their expanded applications in land use. This review reveals that current research still faces challenges such as data uncertainty, necessitating the advancement of more diverse data and new technologies. Future research can enhance the precision and applicability of studies by improving models and methods, integrating big data and multi-scale data, and employing multi-model coupling and various algorithmic experiments for comparison. This would support the advancement of land use spatiotemporal dynamic simulation research to higher levels.

PyMSER─An Open-Source Library for Automatic Equilibration Detection in Molecular Simulations.

Automated molecular simulations are used extensively for predicting material properties. Typically, these simulations exhibit two regimes: a dynamic equilibration part, followed by a steady state. For extracting observable properties, the simulations must first reach a steady state so that thermodynamic averages can be taken. However, as equilibration depends on simulation conditions, predicting the optimal number of simulation steps a priori is impossible. Here, we demonstrate the application of the Marginal Standard Error Rule (MSER) for automatically identifying the optimal truncation point in Grand Canonical Monte Carlo (GCMC) simulations. This novel automatic procedure determines the point at which a steady state is reached, ensuring that figures of merit are extracted in an objective, accurate, and reproducible fashion. In the case of GCMC simulations of gas adsorption in metal-organic frameworks, we find that this methodology reduces the computational cost by up to 90%. As MSER statistics are independent of the simulation method that creates the data, this library is, in principle, applicable to any time series analysis in which equilibration truncation is required. The open-source Python implementation of our method, pyMSER, is publicly available for reuse and validation at https://github.com/IBM/pymser.

Structural Insight into Wild Type of Arginine Deiminase Protein and its Three Mutated Versions in Complex with Arginine Molecule: A Computational Approach

Aims: This study aims to analyze the wild type of arginine deiminase and three mutant forms of it (G320L, A303R, and E304L) in complex with arginine molecule using Molecular Dynamics (MD) simulation to find the best enzyme form with a lower level of energy and more stability. Background: Arginine Deiminase (ADI) is an arginine-degrading enzyme that has an anticancer effect on some cancers, such as Hepatocellular Carcinoma (HCC) and melanoma. Methods: Homology models of native and mutant forms of ADI were generated and evaluated using the I-TASSER and other databases and prediction algorithms. All prepared enzyme structures via homology modeling were used as receptors in the molecular docking and MD simulation steps. Results: MD simulation results demonstrated that the G302L structure has the lowest value of Rg over MD simulation time, indicating its more appropriate intramolecular interactions than other structures. G302L has also established the most desirable interactions with arginine molecules during 80 ns MD simulation so that the G302L/ arginine complex has the maximum intermolecular hydrogen bonds. Arginine molecule in complex with G302L has the best position in the active site of ADI protein. Conclusion: The G302L mutant ADI from Mycoplasma hominis can be a good candidate for better stability in arginine deprivation therapy in arginine auxotrophic cancers.

Stochastic differential equations for non-analog Monte Carlo model of point kinetics equations

The stochastic form of one-point and two-point reactor kinetics models have been extensively investigated in the literature. We have noticed that, in some studies, the probabilistic nature of neutron interaction mechanisms was not incorporated truly. For instance, in the case of negative reactivity insertions, one of the presented probability functions becomes negative, making the Monte Carlo (MC) simulation impossible. In this manuscript, the non-analog stochastic point reactor kinetics models are developed to simulate the stochastic behavior of nuclear systems. To reduce the imposed variance from the leakage and capture of neurons, both capture and leakage events are sampled implicitly. Precursors are also forced to decay in each simulation step. The true variances of the neutron’s and delayed neutron precursor’s populations are determined from the solution of the developed non-analog stochastic models. Also, they are compared with those of both analog and non-analog Monte Carlo methods. The results show that the analog Monte Carlo (MC) has a larger variance than the non-analog MC.

Optimization model for urban ecological network connectivity considering geospatial constraints

ABSTRACT Connectivity enhancement of ecological networks is an efficient and advantageous spatial planning strategy, and it is a hot research topic to analyze ecological networks based on complex systems theory and perform model optimization. As a typical geographic network, the evolution process of ecological network, such as node and edge growth, will be subject to geospatial constraints, and the current research lacks optimization models that take the geospatial constraints into account. This study focused on urban ecological network connectivity and the distribution of nodes as well as structural fractures where the connectivity of the ecological network was hindered were analyzed. Then it introduced a local world network model to optimize network connectivity and designed a network growth mechanism with limited added nodes and optimal connections. An ecological network optimization model considering geospatial constraints was constructed, and the network closeness index was used to fit the objective function curve to select optimization schemes. This study used Shenzhen, China as a case study, and the results indicated that the initial ecological network was significantly fractured by urban construction. In the optimization scenario, the network evolution reached its optimal state at the simulation step size of t = 26. At this point, the closeness index of the optimized network was increased by 15%. The optimization model constructed in this paper emphasizes the importance of functional restoration of existing network nodes, and the results of the study can provide support for ecospatial layout optimization and management.

Efficient Mixed-Precision Matrix Factorization of the Inverse Overlap Matrix in Electronic Structure Calculations with AI-Hardware and GPUs.

In recent years, a new kind of accelerated hardware has gained popularity in the artificial intelligence (AI) community which enables extremely high-performance tensor contractions in reduced precision for deep neural network calculations. In this article, we exploit Nvidia Tensor cores, a prototypical example of such AI-hardware, to develop a mixed precision approach for computing a dense matrix factorization of the inverse overlap matrix in electronic structure theory, S-1. This factorization of S-1, written as ZZT = S-1, is used to transform the general matrix eigenvalue problem into a standard matrix eigenvalue problem. Here we present a mixed precision iterative refinement algorithm where Z is given recursively using matrix-matrix multiplications and can be computed with high performance on Tensor cores. To understand the performance and accuracy of Tensor cores, comparisons are made to GPU-only implementations in single and double precision. Additionally, we propose a nonparametric stopping criteria which is robust in the face of lower precision floating point operations. The algorithm is particularly useful when we have a good initial guess to Z, for example, from previous time steps in quantum-mechanical molecular dynamics simulations or from a previous iteration in a geometry optimization.

Optimal Coordinated Operation for Hydro–Wind Power System

The intermittent and stochastic characteristics of wind power pose a higher demand on the complementarity of hydropower. Studying the optimal coordinated operation of hydro–wind power systems has become an extremely effective way to create safe and efficient systems. This paper aims to study the optimal coordinated operation of a hybrid power system based on a newly established Simulink model. The analysis of the optimal coordinated operation undergoes two simulation steps, including the optimization of the complementary mode and the optimization of capacity allocation. The method of multiple complementary indicators is adopted to enable the optimization analysis. The results from the complementary analysis show that the hydraulic tracing effect obviously mitigates operational risks and reduces power losses under adverse wind speeds. The results from the analysis of capacity allocation also show that the marginal permeation of installed wind capacity will not exceed 250 MW for a 100 MW hydropower plant under random wind speeds. These simulation results are obtained based on the consideration of some real application scenarios, which help power plants to make the optimal operation plan with a high efficiency of wind energy and high hydro flexibility.

Molecular Dynamics Study of Adhesion-Related Structural Properties for PVDF-HFP Polymer Binders

The current lithium-ion battery (LIB) market is expanding rapidly. Binders are essential to maintaining the high capacity and longevity of batteries. These binders can be classified into organic binders used for cathodes and aqueous binders used for anodes. Among them, Poly(vinylidene fluoride) (PVDF) binder is a representative organic binder that offers excellent electrochemical stability. To solve low adhesive force and weak mechanical strength of conventional PVDF binders, hexafluoropropylene (HFP) is added to improve these properties. However, the present poly(vinylidene fluoride-hexafluoropropylene) (PVDF-HFP) binders still show lower adhesive force than other polymeric binders, and therefore further research works are necessary.In this study, we employ classical molecular dynamics (MD) simulations to investigate the structural properties of the PVDF-HFP copolymers. To create realistic disordered PVDF-HFP polymers, we develop a computational scheme that can randomly arrange the angles and sequences of the constituent polymer chains. By varying the ratio of randomly arranged PVDF-HFP copolymers to 0/100, 5/95, 10/90, 15/85, and 20/80, we build a solution model that mimics the HFP concentration of PVDF-HFP (0–20%, Sigma Aldrich) used in the actual experiments. N-methyl-2-pyrrolidone (NMP) used as solvents. To create a large-scale model, we utilize the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) and set parameters by using OPLS force field. This allows us to build a simulation model containing approximately 40,000 atoms consisting of 10 PVDF-HFP polymers and 1500 NMP monomers.After creating the simulation model, approximately eight steps of molecular dynamics simulation are performed by referring to the process of making the actual polymer film. In this process, NMP molecules are evaporated to produce a polymer film consisting only of PVDF-HFP polymers. Then we perform the adhesion analysis by applying forces to five polymer film models with different composition ratios in various conditions. Throughout these processes, we find the characteristic changes depending on the composition ratio. Finally, we will present the possible routes that can lead to the enhanced adhesive properties of PVDF-HFP, which is a key requirement to the development of high performance binders.

Dynamic Activity and Stability of Transition Metal (oxy)Hydroxide Oxygen Evolution Electrocatalysts Under Steady and Intermittent Operation

The oxygen evolution reaction (OER) plays a critical role in several energy conversion technologies, requiring catalysts that combine high activity with stability for successful commercialization. Catalysts from first-row transition metal oxides have garnered considerable attention in this context. Their noteworthy OER activity and stability in alkaline environments position them as a more economical option than their counterparts in acidic media. However, recent studies have shown that these materials tend to experience significant dissolution issues and undergo changes in their chemical composition, a phenomenon observed even during steady-state operation.1–3 Addressing these challenges is essential for advancing commercial energy conversion technologies, which often face intermittent loads and reverse currents. A deeper understanding of the dynamic reconstruction under fluctuating conditions is crucial for developing more durable and efficient electrocatalytic materials.4 In this work, we investigate the dynamic performance of nickel cobalt (oxy)hydroxide electrocatalytic films containing iron active sites in alkaline media under steady and intermittent operation. First, we systematically examine the changes in OER catalytic activity in a typical three-electrode configuration, focusing on the incorporation and redissolution of iron and cerium during different electrochemical conditioning methods. As shown in Figure 1a, conditioning using chronopotentiometry decreases the OER overpotential without significantly shifting the redox peak. In contrast, conditioning using cyclic voltammetry significantly shifts the redox peak position (Figure 1b). These differences are attributed to the (oxy)hydroxide film thickness, which forms in situ during conditioning and affects the Fe dissolution and incorporation rates based on the conditioning method.5 We use different analytical techniques to probe chemical transformations during conditioning. Time-of-flight secondary ion mass spectrometry (TOF-SIMS) and X-ray photoelectron spectroscopy (XPS) are utilized to analyze compositional changes in the electrocatalytic films. Moreover, inductively coupled plasma mass spectrometry (ICP-MS) tracks metal dissolution.Next, we utilize an electrochemical flow electrolyzer with a zero-gap configuration, integrated with online ICP-MS and post-experimental TOF-SIMS and XPS, to study the reconstruction of these electrocatalysts under intermittent conditions. A NiFe anode operating in 6 M KOH with 1 ppm of Fe3+ shows a 40-mV change after 120 cycles of alternating low and high currents, including a simulated shutdown step (Figure 1c). This cell potential change, becoming more negative with continued cycling (Figure 1d), is attributed to the shutdown step. Our findings underscore how operating conditions affect the dynamic reconstruction of electrocatalytic materials, offering critical insights for developing more robust and resilient materials suitable for commercial applications.

Optimized Real-Time Stochastic Model of Power Electronic Converters based on FPGA

Stochastic models can effectively describe the operating characteristics of power electronic converters with stochastic parameters. However, it is difficult to implement the models in FPGA-based real-time simulation (RTS) because their high order leads to a large calculation. This paper proposes an optimized real-time stochastic modeling method for power electronic converters based on generalized polynomial chaos (gPC). Firstly, an orthogonal polynomials construction method is used based on Schmidt orthogonalization to describe stochastic variables with atypical probability distributions and provide conditions for simplifying the system model. Secondly, the method of probability space transformation is adopted to divide the system model into multiple sub-models to suppress the exponential growth of the model order while maintaining the statistical properties. This method has performed over the traditional stochastic modeling method. The proposed model is built on FPGA-based hardware-in-the-loop (HIL) experiment platform with 1us simulation step. The optimized model uses approximately 37% fewer resources than the traditional stochastic model while maintaining the same level of accuracy.

Quantifying Patient Capabilities and Setting the Stage for Future Development: Insights from a Sensor-Augmented Simulated Use Study with Pen Injectors.

While formative and summative usability testing is essential to confirm safe and effective product use, it may not be sufficient to comprehensively understand user capabilities and limitations in device interactions. Therefore, this research aims to quantify user handling forces for different device handling steps of pen injectors through sensor-augmented simulated use studies. The study involved 46 participants who were divided into two groups: a healthy control group and a group of users with dexterity impairments. All participants were instructed to perform simulated handling steps using non-functional dummy devices equipped with force and torque sensors. Each handling step was performed twice: first at what participants considered a comfortable force level and then at their maximum force. The study then analyzed force data to investigate the impact of user characteristics and device geometry on force exertion during the different handling steps. The study demonstrates differences in the perceived comfortable and maximum force levels between the control and patient groups. These force levels decrease slightly with the user's age and level of dexterity impairment. Furthermore, the forces applied by the users are dependent on the geometry of the device and the holding pattern. The results highlight the significance of sensor-augmented simulated use studies as a tool for providing quantitative insights into users' ability to exert force while handling self-injection devices. These data offer comprehensive insights that inform the definition of performance requirements and specifications for injection device design, thereby supporting the advancement of future self-injection devices.

Microstructural Evolution of Tantalum During Deformation and Subsequent Annealing

Microstructure-aware models are necessary to predict the behavior of material based on process knowledge or to extrapolate mechanical properties of materials to environmental conditions which are not easily reproduced in the laboratory, e.g., nuclear reactor environments. Elemental Ta provides a relatively simple BCC system in which to develop a microstructural understanding of deformation processes which can then be applied to more complicated BCC alloys. In situ neutron diffraction during compressive deformation and subsequent heat treatment have been used to monitor the evolution of microstructural features in Ta throughout simulated processing steps. Crystallographic texture and dislocation density are determined as a function of first plastic strain, then temperature. Lattice strains are determined and attributed to stresses at macroscopic, grain and dislocation length scales. The increase of the dislocation density through deformation and subsequent recovery during heat treatment is monitored through the changing diffraction line profile. Also, randomization of the texture is used as a signature of recrystallization. The recovery of dislocations through annihilation is not observed to depend on the initial dislocation density in the range studied here. In contrast, recrystallization is observed to depend strongly on the initially dislocation density.