Improvement In Computational Efficiency Research Articles

TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations.

Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents substantial advancements in TorchMD-Net software, a pivotal step forward in the shift from conventional force fields to neural network-based potentials. The evolution of TorchMD-Net into a more comprehensive and versatile framework is highlighted, incorporating cutting-edge architectures such as TensorNet. This transformation is achieved through a modular design approach, encouraging customized applications within the scientific community. The most notable enhancement is a significant improvement in computational efficiency, achieving a very remarkable acceleration in the computation of energy and forces for TensorNet models, with performance gains ranging from 2× to 10× over previous, nonoptimized, iterations. Other enhancements include highly optimized neighbor search algorithms that support periodic boundary conditions and smooth integration with existing molecular dynamics frameworks. Additionally, the updated version introduces the capability to integrate physical priors, further enriching its application spectrum and utility in research. The software is available at https://github.com/torchmd/torchmd-net.

Cryptanalysis with improvement on lattice‐based authenticated key exchange protocol for mobile satellite communication networks

SummaryThe mobile satellite communication (MSC) system represents a crucial means of communication, facilitating connections between mobile users and network control centres through satellites. Given the wireless nature of satellite communication, ensuring communication security is paramount for maintaining accountability. Key exchange and authentication (KEA) mechanisms are commonly employed to secure data transmitted over insecure or open channels. Developing a lattice‐based KEA protocol for MSC systems is imperative to achieve both quantum security and efficient communication. In this direction, Yadav et al. devised ring learning with an error‐based KEA technique to ensure a quantum‐safe environment. Their scheme is quantum‐safe and satisfies desirable security attributes but has low efficiency in computation. Moreover, we have identified that their scheme is susceptible to denial of service attack and session key established at user and NCC ends is different. To overcome the flaws of Yadav et al., we propose a lattice‐based KEA protocol for the MSC system to improve computation efficiency, and get resistance against key mismatch attack. The security analysis of the proposed scheme is presented in the random oracle model. Comparative study is also presented to observe advantages in computation efficiency.

Reliability analysis of complex systems using subset simulations with Hamiltonian Neural Networks

We present a new Subset Simulation approach using Hamiltonian neural network-based Monte Carlo sampling for reliability analysis. The proposed strategy combines the superior sampling of the Hamiltonian Monte Carlo method with computationally efficient gradient evaluations using Hamiltonian neural networks. This combination is especially advantageous because the neural network architecture conserves the Hamiltonian, which defines the acceptance criteria of the Hamiltonian Monte Carlo sampler. Hence, this strategy achieves high acceptance rates at low computational cost. Our approach estimates small failure probabilities using Subset Simulations. However, in low-probability sample regions, the gradient evaluation is particularly challenging. The remarkable accuracy of the proposed strategy is demonstrated on different reliability problems, and its efficiency is compared to the traditional Hamiltonian Monte Carlo method. We note that this approach can reach its limitations for gradient estimations in low-probability regions of complex and high-dimensional distributions. Thus, we propose techniques to improve gradient prediction in these particular situations and enable accurate estimations of the probability of failure. The highlight of this study is the reliability analysis of a system whose parameter distributions must be inferred with Bayesian inference problems. In such a case, the Hamiltonian Monte Carlo method requires a full model evaluation for each gradient evaluation and, therefore, comes at a very high cost. However, using Hamiltonian neural networks in this framework replaces the expensive model evaluation, resulting in tremendous improvements in computational efficiency.

Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications

Abstract. The wflow_sbm hydrological model, recently released by Deltares, as part of the Wflow.jl (v0.7.3) modelling framework, is being used to better understand and potentially address multiple operational and water resource planning challenges from a catchment scale to national scale to continental and global scale. Wflow.jl is a free and open-source distributed hydrological modelling framework written in the Julia programming language. The development of wflow_sbm, the model structure, equations and functionalities are described in detail, including example applications of wflow_sbm. The wflow_sbm model aims to strike a balance between low-resolution, low-complexity and high-resolution, high-complexity hydrological models. Most wflow_sbm parameters are based on physical characteristics or processes, and at the same time wflow_sbm has a runtime performance well suited for large-scale high-resolution model applications. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets and through the use of point-scale (pedo)transfer functions and suitable upscaling rules and generally result in a satisfactory (0.4 ≥ Kling–Gupta efficiency (KGE) < 0.7) to good (KGE ≥ 0.7) performance for discharge a priori (without further tuning). Wflow_sbm includes relevant hydrological processes such as glacier and snow processes, evapotranspiration processes, unsaturated zone dynamics, (shallow) groundwater, and surface flow routing including lakes and reservoirs. Further planned developments include improvements on the computational efficiency and flexibility of the routing scheme, implementation of a water demand and allocation module for water resource modelling, the addition of a deep groundwater concept, and computational efficiency improvements through for example distributed computing and graphics processing unit (GPU) acceleration.

A Privacy Protection Scheme of Certificateless Aggregate Ring Signcryption Based on SM2 Algorithm in Smart Grid

With the rapid increase in smart grid users and the increasing cost of user data transmission, proposing an encryption method that does not increase the construction cost while increasing the user ceiling has become the focus of many scholars. At the same time, the increase in users will also lead to more security problems, and it is also necessary to solve the privacy protection for users during information transmission. In order to solve the above problems, this paper proposes an aggregated ring encryption scheme based on the SM2 algorithm with special features, referred to as SM2-CLARSC, based on the certificateless ring signcryption mechanism and combining with the aggregate signcryption. SM2-CLARSC is designed to satisfy the basic needs of the smart grid, and it can be resistant to replay attacks, forward security and backward security, etc. It has better security and higher efficiency than existing solutions. Comparing SM2-CLARSC with existing typical solutions through simulation, the result proves that this solution has more comprehensive functions, higher security, and significant computational efficiency improvement.

Large language models for biomedicine: foundations, opportunities, challenges, and best practices.

Generative large language models (LLMs) are a subset of transformers-based neural network architecture models. LLMs have successfully leveraged a combination of an increased number of parameters, improvements in computational efficiency, and large pre-training datasets to perform a wide spectrum of natural language processing (NLP) tasks. Using a few examples (few-shot) or no examples (zero-shot) for prompt-tuning has enabled LLMs to achieve state-of-the-art performance in a broad range of NLP applications. This article by the American Medical Informatics Association (AMIA) NLP Working Group characterizes the opportunities, challenges, and best practices for our community to leverage and advance the integration of LLMs in downstream NLP applications effectively. This can be accomplished through a variety of approaches, including augmented prompting, instruction prompt tuning, and reinforcement learning from human feedback (RLHF). Our focus is on making LLMs accessible to the broader biomedical informatics community, including clinicians and researchers who may be unfamiliar with NLP. Additionally, NLP practitioners may gain insight from the described best practices. We focus on 3 broad categories of NLP tasks, namely natural language understanding, natural language inferencing, and natural language generation. We review the emerging trends in prompt tuning, instruction fine-tuning, and evaluation metrics used for LLMs while drawing attention to several issues that impact biomedical NLP applications, including falsehoods in generated text (confabulation/hallucinations), toxicity, and dataset contamination leading to overfitting. We also review potential approaches to address some of these current challenges in LLMs, such as chain of thought prompting, and the phenomena of emergent capabilities observed in LLMs that can be leveraged to address complex NLP challenge in biomedical applications.

An efficiency-improved GPU algorithm for the 2 + 2 + 1 method in nonlinear beamforming

Abstract Nonlinear beamforming (NLBF) has emerged as a highly effective technology for enhancing seismic data quality. The crux of NLBF's success lies in its ability to robustly estimate local traveltime operators directly from input data, a process that entails solving millions or even billions of nonlinear optimization problems per input gather. Among the solvers utilized for estimating these operators is the 2 + 2 + 1 method, for which we have previously introduced algorithmic implementations on both the CPU and GPU platforms. In this paper, we present an efficiency-improved GPU algorithm for the 2 + 2 + 1 method, particularly beneficial when dealing with small data apertures in NLBF. Our enhanced GPU algorithm brings significant improvements in computation efficiency through several strategic measures, which include leveraging Horner's method to minimize the mathematical overhead of traveltime calculation, implementing a GPU-friendly data reduction algorithm to exploit GPU computational power, and optimizing shared GPU memory usage as the primary workspace whenever feasible. To demonstrate the tangible efficiency enhancement achieved by our new GPU algorithm, via two illustrative examples, we compare its performance with that of our previous implementation.

Multiphysics simulation of two-phase viscous fluid flow steered by electric field for jetting of microdroplets

In this research, we delve into the intricacies of viscous fluid flow with electric field coupling by employing the Finite Element Method (FEM) in tandem with the level set method. We generate a weak form for satisfying governing equations for electric field and fluid velocity while two phases are tracked by the level set function. The primary focus of this study is the complex interactions between free-falling jet and electric field, and the behavior of droplet encompassing deformation, fission, and fusion under the influence of electric field. The main contribution of this paper is given a new implement by using the P1/P1 scheme to directly solve the weak forms of coupled governing equations, which significantly improves calculation efficiency compared to the P2/P1 scheme, and we open source the code. This implement is verified by comparing with the experimental results of oil droplets deforming under an electric field. Computations are performed by FEniCS open-source packages. The phenomena documented underscore the multifaceted relationship between electrodynamic forces and fluid mechanics, accentuated distinctly under non-uniform electric field conditions.

Efficient and lightweight in-memory computing architecture for hardware security

This paper introduces an innovative solution for improving the efficiency and speed of the Advanced Encryption Standard (AES) based cryptographic algorithm. The approach leverages in-memory computing (IMC) and is versatile for application across a broad spectrum of IoT applications, including robotic autonomous vehicles and various other scenarios. To achieve this goal, memristor (MR) designs are proposed to emulate the arithmetic operations required for different phases of the AES algorithm, enabling efficient in-memory processing. The key contributions of this work include; 1) The development of a 4 bit-MR state element for implementing different arithmetic operations in an AES hardware prototype; 2) The creation of a pipeline AES design for massive parallelism and MR integration compatibility; and 3) The hardware implementation of the AES-IMC based architecture using the MR emulator. The results show that AES-IMC performs better than existing architectures in terms of higher throughput and energy efficiency. Compared to conventional AES hardware, AES-IMC achieves a 30% power enhancement with comparable throughput. Additionally, when compared to state-of-the-art AES-based NVM engines, AES-IMC demonstrates comparable power dissipation and a 62% increase in throughput. The IMC architecture enables cost-effective real-time deployment of AES, leading to high-performance computing. By leveraging the power of in-memory computing, this system is able to provide improved computational efficiency and faster processing speeds, making it a promising solution for a wide range of applications in the field of autonomous driving and robotics. The potential benefits of this system include improved safety and security of unmanned devices, as well as enhanced performance and cost-effectiveness in a variety of computing environments.

Reference monopile designs for US East Coast sites supporting the IEA 15 MW reference turbine using a novel conceptual design methodology

A novel conceptual design methodology was developed and implemented to design twelve reference monopile foundations, compatible with the IEA 15 MW reference turbine. The designs are position-specific, corresponding to planned, US East Coast, wind energy development areas. The monopiles were designed considering the fatigue limit state (FLS), ultimate limit state (ULS), serviceability limit state (SLS), and natural frequency analysis (NFA), following an intentionally reduced subset of design load cases (DLCs) in IEC 61400-3-1. In 11 of 12 cases, the diameter and wall thicknesses of the monopile foundations were found to be determined by FLS criteria. The exception was one position subjected to a combination of breaking waves and a large significant wave height. For the 11 positions governed entirely by FLS, the maximum ULS utilization ratios varied between 0.63 and 0.81, indicating significant reserve capacity for the ultimate limit state. Fatigue damage during turbine idling was found to be an important contributor to lifetime fatigue damage, despite having a relatively small probability of occurrence. The novel design methodology described herein includes a preliminary FLS assessment step, which was found to be an efficient way to select an initial foundation geometry, while providing a 92% improvement in computational efficiency.

Numerical and experimental investigation of stratified water storage tanks: An enhanced adaptive-grid model

Stratified water storage tanks are key in thermal energy systems, effectively balancing energy supply with heat demand, thus facilitating operational flexibility. Accurately modeling both energy balance and thermocline evolution with minimal computational effort is essential for models implementation in real-time control applications. One-dimensional, static-grid models are favored for their easy implementation; however, they require dense grids in order to minimize numerical diffusion and to represent mixing effect correctly, resulting in rather expensive computational effort. This study introduces an adaptive-grid model that comprehensively represents the water storage tank’s geometrical features, including inlet and outlet duct heights and dead volume sizes. It also details the spatially discretized heat diffusion and characterizes the inflow region’s mixing using an empirical equation. Implemented in both TRaNsient SYstem Simulation (TRNSYS) and Modelica environments, the model is validated through laboratory experiments with a hot water storage tank operating under various mass flow and temperature conditions, representative of heat pump-driven heating systems. The results indicate improvements in computational efficiency compared to static-grid models, specifically a 55% reduction in simulation duration with the same level of uncertainty.

Joint Decisions of Components Replacement and Spare Parts Ordering Considering Different Supplied Product Quality

In addition to equipment maintenance decisions, spare parts ordering decisions from different suppliers play a key role in reducing related costs (e.g., maintenance, inventory and ordering costs). Since suppliers may use different production technologies and materials, spare parts (or products) from different suppliers can be different in quality. Nevertheless, in recent studies, the quality of spare parts is rarely considered to incorporate both equipment maintenance and spare parts ordering. In this paper, we investigate the joint optimization of condition-based maintenance and spare parts provisioning policy under two suppliers with different product quality. We formulate a sequential-decision problem with a Markov decision process and consequently obtain an optimal maintenance and ordering policy by an exact value iteration algorithm. To improve computation efficiency, based on the principle of sequential optimization, we develop heuristic methods. Extensive numerical experiments are conducted to assess the overall performance of the developed heuristic methods. Compared to the optimal method, results showed that the average cost gap is about 2% and computation time is reduced by 94% on average under the proposed heuristic method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the observation that automobile industries tried to integrate emergency suppliers from which spare parts have different quality into maintenance schedules to avoid stockout and reduce equipment failure during the Covid-19 pandemic. Specifically, the article focuses on balancing the trade-offs between condition-based maintenance and inventory management from two suppliers with different lead times and spare parts quality for multi-unit systems. On the one hand, effective maintenance scheduling relies on spare parts for replacement to ensure the stability of production. On the other hand, inventory management needs to select the supplier with appropriate lead time and product quality to reduce the ordering cost and avoid stockout based on the degradation states of equipment. The joint optimization of these two aspects serves to reduce the total maintenance and ordering cost. Nevertheless, most existing research aims to optimize them separately. In this paper, we formulate the joint decision problem considering the two aspects based on a Markov decision process. We obtain an optimal maintenance and ordering policy by an exact value iteration algorithm and present heuristics to improve the computation efficiency when the system contains multiple machines. Practitioners can implement the proposed methodology to make condition-based maintenance and inventory management when spare parts with different qualities are ordered from two suppliers. To balance cost and computational efficiency, it is suggested to implement the optimal policy by an exact value iteration algorithm when the number of machines is small in the system and use the heuristic methods when the number of machines is large (i.e., usually larger than 3).

Graph Neural Network for domain segmentation to predict regions of non-ideal mixing in two-dimensional baffle flow systems

This paper presents a novel approach to address computational challenges in predicting flow features by employing a Graph Neural Network (GNN), which is proficient in predicting flow domain values. Traditional Computational Fluid Dynamics (CFD) simulations, although effective, often require substantial computational resources and time, limiting their applicability in time-sensitive scenarios and optimization studies necessitating extensive case studies. The main objective was to evaluate the feasibility of employing node classification on a graph generated from a 2D baffle flow system to segment the domain based on relative fluid age. A second objective was to compare the computational time required for CFD simulations with the inference time of the network to quantify the efficiency gains achieved by utilizing the network. Results demonstrate the potential of utilizing graph convolutional networks for domain segmentation to predict regions of holdup and bypass. The GNN achieved 97% and 92% accuracy in predicting recirculation regions in single and double baffle cases, particularly excelling in high Reynolds number scenarios. Importantly, the proposed GNN-based approach reduces computation time by over 2,100%, showcasing significant efficiency gains. Results here highlight the promise of employing graph convolutional networks for flow feature prediction, offering substantial computational efficiency improvements over traditional CFD simulations.

Chemical kinetic model reduction based on species‐targeted local sensitivity analysis

AbstractReduction of large combustion mechanisms is usually conducted based on the detection and elimination of redundant species and reactions. Reaction elimination methods are mostly based on sensitivity analysis, which can provide insight into the kinetic system, while species elimination methods are more efficient. In this work, the species‐targeted local sensitivity analysis (STLSA) method is proposed to evaluate the importance of species and eliminate non‐crucial species and their related reactions to simplify kinetic models. This paper comprehensively evaluates the effectiveness of STLSA across various combustion scenarios, including high and low‐temperature ignition and laminar flame speed, using diverse mechanisms like USC Mech II, JetSurf 1.0, POLIMI_TOT_1412, NUIGMech1.1 and so on. Comparisons with graph‐based methods, such as DRG and DRGEP, highlight STLSA's superior efficiency and accuracy. Moreover, STLSA is compared to species‐targeted global sensitivity analysis (STGSA), demonstrating significant computation cost savings and comparable model reduction capabilities. The study concludes that STLSA is a robust and versatile tool for mechanism reduction, offering substantial improvements in computational efficiency while maintaining high accuracy in predicting key combustion properties.

Super-resolution techniques for biomedical applications and challenges.

Super-resolution (SR) techniques have revolutionized the field of biomedical applications by detailing the structures at resolutions beyond the limits of imaging or measuring tools. These techniques have been applied in various biomedical applications, including microscopy, magnetic resonance imaging (MRI), computed tomography (CT), X-ray, electroencephalogram (EEG), ultrasound, etc. SR methods are categorized into two main types: traditional non-learning-based methods and modern learning-based approaches. In both applications, SR methodologies have been effectively utilized on biomedical images, enhancing the visualization of complex biological structures. Additionally, these methods have been employed on biomedical data, leading to improvements in computational precision and efficiency for biomedical simulations. The use of SR techniques has resulted in more detailed and accurate analyses in diagnostics and research, essential for early disease detection and treatment planning. However, challenges such as computational demands, data interpretation complexities, and the lack of unified high-quality data persist. The article emphasizes these issues, underscoring the need for ongoing development in SR technologies to further improve biomedical research and patient care outcomes.

DRCLAS: An efficient certificateless aggregate signature scheme with dynamic revocation in vehicular ad-hoc networks

The emergence of the Vehicular Ad-hoc Networks (VANETs) has greatly enhanced the efficiency and safety of transportation systems. However, due to the dynamicness and openness of vehicular networks, it is vulnerable for VANETs to confront various security threats. The dissemination of disinformation and the occurrence of attacks within these networks can result in severe damage and compromise the overall security of transportation systems. To mitigate such challenges, this paper presents an efficient certificateless aggregate signature scheme with dynamic revocation in VANETs. Taking use of the early-stopping factorial bitwise divisions (EFBD) algorithm, it achieves the invalid signatures tracking, and revokes the malicious vehicles dynamically. In addition, by combining the fingerprint counting bloom filter (FP-CBF), the trust assessment mechanism and the cloud server, it enables the vehicle to remedy its incorrect signatures. At last, through the comprehensive security and performance analysis, it demonstrates that this protocol enjoys improved communication security and computational efficiency.

A Gaussian mixture multiple-model belief propagation filter for multisensor-multitarget tracking

This paper presents a novel Gaussian mixture multi-model belief propagation (GMM-BP) filter for maneuvering multitarget tracking with multiple sensors. The filter is built upon the BP-based multisensor-multitarget tracking scheme, enabling accurate estimation of target numbers and states. It assumes linear Gaussian target motion, birth process, and sensor measurement models and utilizes the Gaussian mixture model to represent the target's marginal posterior probability density function. Additionally, multiple models are incorporated for target motion, enhancing the maneuvering target tracking capability. Closed-form recursions for means, covariances, and weights of Gaussian components in the filter are derived. To enhance scalability with the number of sensors, a simplified multi-sensor data fusion process is proposed, thereby preserving a linear relationship between Gaussian components and the number of sensors. The performance of the filter was evaluated in a three-dimensional (3D) simulated environment. Compared to the particle-implemented BP filter and the labeled multi-Bernoulli (LMB) filter, the proposed GMM-BP filter exhibited a significant improvement in computational efficiency while maintaining a high level of tracking accuracy.

A reactive molecular dynamics model for uranium/hydrogen containing systems.

Uranium-based materials are valuable assets in the energy, medical, and military industries. However, understanding their sensitivity to hydrogen embrittlement is particularly challenging due to the toxicity of uranium and the computationally expensive nature of quantum-based methods generally required to study such processes. In this regard, we have developed a Chebyshev Interaction Model for Efficient Simulation (ChIMES) that can be employed to compute energies and forces of U and UH3 bulk structures with vacancies and hydrogen interstitials with accuracy similar to that of Density Functional Theory (DFT) while yielding linear scaling and orders of magnitude improvement in computational efficiency. We show that the bulk structural parameters, uranium and hydrogen vacancy formation energies, and diffusion barriers predicted by the ChIMES potential are in strong agreement with the reference DFT data. We then use ChIMES to conduct molecular dynamics simulations of the temperature-dependent diffusion of a hydrogen interstitial and determine the corresponding diffusion activation energy. Our model has particular significance in studies of actinides and other high-Z materials, where there is a strong need for computationally efficient methods to bridge length and time scales between experiments and quantum theory.

Method for GPU-based spectral data cube reconstruction of integral field snapshot imaging spectrometers.

In this paper, the principles of spectral data cube reconstruction based on an integral field snapshot imaging spectrometer and GPU-based acceleration are presented. The primary focus is on improving the reconstruction algorithm using GPU parallel computing technology to enhance the computational efficiency for real-time applications. And the computational tasks of the spectral reconstruction algorithm were transferred to the GPU through program parallelization and memory optimization, resulting in significant performance gains. Experimental results indicate that the average processing time of the GPU-based parallel algorithm is approximately 29.43 ms, showing a substantial acceleration ratio of about 14.27 compared to the traditional CPU serial algorithm with an average processing time of around 420.46 ms. The study aims to refine the GPU parallelization algorithm for continued improvement in computational efficiency and overall performance. The anticipated applications of this research include providing crucial technical support for the perception and monitoring of crop growth traits in agricultural production, contributing to the modernization and advancement of intelligence in the field.

Multiscale Simulation of the Coupled Chemo-Mechanical Behavior of Porous Electrode Materials by Direct FE2 Method

Application of porous electrode materials has sparked significant interest as a strategy to mitigate traditional electrode mechanical failure arising from its intercalation-induced large volume change. In this work, a thermal analogy method is employed for implementing the coupled chemo-mechanical model into the finite element (FE) package ABAQUS via user subroutines UMATHT and UMAT, which is used to model the lithium (Li) diffusion and the resulting deformation of the electrode during charge-discharge cycling. This work presents a Direct FE2 method for modeling the chemo-mechanically coupled behavior of porous electrode materials by establishing the macro-microscopic scale transitions through concentration and displacement DOFs and the representative volume element (RVE) volume scaling relationship. The two-scale numerical simulations can be implemented in a single computational scheme. Within the present computational framework, the Li diffusion and mechanical deformation in the porous silicon electrode during charging and discharging are easily simulated in the typical FE package. Benchmarked against the traditional direct full-field numerical computational method, the Direct FE2 method is validated to present significant computational efficiency improvements through two numerical examples, the constrained expansion and the pre-compression expansion of porous electrode, by 99.27% and 94.55%, respectively, while maintaining the high precision.