Computational Mechanics Research Articles

Lateral prefrontal theta oscillations causally drive a computational mechanism underlying conflict expectation and adaptation.

Adapting our behavior to environmental demands relies on our capacity to perceive and manage potential conflicts within our surroundings. While evidence implicates the involvement of the lateral prefrontal cortex and theta oscillations in detecting conflict stimuli, their causal role in conflict expectation remains elusive. Consequently, the exact computations and neural mechanisms underlying these cognitive processes still need to be determined. We employed an integrative approach involving cognitive computational modeling, fMRI, TMS, and EEG to establish a causal link between oscillatory brain function, its neurocomputational role, and the resulting conflict processing and adaptation behavior. Our results reveal a computational process underlying conflict expectation, which correlates with BOLD-fMRI and theta activity in the superior frontal gyrus (SFG). Modulation of theta activity via rhythmic TMS applied over the SFG induces endogenous theta activity, which in turn enhances computations associated with conflict expectation. These findings provide evidence for the causal involvement of SFG theta activity in learning and allocating cognitive resources to address forthcoming conflict stimuli.

The Distributed Xin’anjiang Model Incorporating the Analytic Solution of the Storage Capacity Under Unsteady-State Conditions

Developing a functional linkage between hydrological variables and easily accessible terrain and soil information is a novel concept for distributed hydrological models. This approach aims to address limitations imposed by data scarcity and high computational demands. The model hypothesizes that the relationship between the evaporation flux and the absolute value of the matric potential follows a power exponential pattern. Analytic solutions for the groundwater depth, the evaporation capacity, and the storage capacity are derived with respect to the topographic index, considering the relationship between the groundwater depth and the topographic index and the influence of setting off. Subsequently, a distributed Xin’anjiang Model using the analytic solution of the storage capacity under unsteady-state conditions is constructed. This new model is employed to simulate soil moisture and discharge in the Tarrawarra Watershed. The simulation results for soil moisture and discharge are compared with those from the Storage Capacity Model and the DHSVM. Additionally, the computational speeds of all three models are compared. The findings indicate that the simulation accuracy of the new model for soil moisture and discharge surpasses that of the Storage Capacity Model and the DHSVM. Meanwhile, the computational speed of the new model is significantly faster than the DHSVM and slightly slower than the Storage Capacity Model. It offers a balance between computational efficiency, predictive accuracy, and physical mechanism representation. The data requirements of the new model are minimal and easy to procure, and it requires less computational effort. Moreover, it accurately captures the spatial and temporal dynamics of soil moisture and the discharge process of the watershed.

Decomposing dynamical subprocesses for compositional generalization

A striking feature of human cognition is an exceptional ability to rapidly adapt to novel situations. It is proposed this relies on abstracting and generalizing past experiences. While previous research has explored how humans detect and generalize single sequential processes, we have a limited understanding of how humans adapt to more naturalistic scenarios, for example, complex, multisubprocess environments. Here, we propose a candidate computational mechanism that posits compositional generalization of knowledge about subprocess dynamics. In two samples (N = 238 and N = 137), we combined a novel sequence learning task and computational modeling to ask whether humans extract and generalize subprocesses compositionally to solve new problems. In prior learning, participants experienced sequences of compound images formed from two graphs' product spaces (group 1: G1 and G2, group 2: G3 and G4). In transfer learning, both groups encountered compound images from the product of G1 and G3, composed entirely of new images. We show that subprocess knowledge transferred between task phases, such that in a new task environment each group had enhanced accuracy in predicting subprocess dynamics they had experienced during prior learning. Computational models utilizing predictive representations, based solely on the temporal contiguity of experienced task states, without an ability to transfer knowledge, failed to explain these data. Instead, behavior was consistent with a predictive representation model that maps task states between prior and transfer learning. These results help advance a mechanistic understanding of how humans discover and abstract subprocesses composing their experiences and compositionally reuse prior knowledge as a scaffolding for new experiences.

The Multiple Dimensions of Familiarity: From Representations to Phenomenology.

This article focuses on familiarity, the form of memory allowing humans to recognize stimuli that have been encountered before. We aim to emphasize its complex nature which includes representational and phenomenological dimensions. The former implies that its neural correlates depend on the type and complexity of the cue stimulus, as different classes of stimuli are represented in distributed ventral visual and medial temporal regions. The second dimension relates to the subjective feeling of familiarity, which results from a fluency signal that is attributed to past encounters with the stimulus. We review mnemonic and non-mnemonic sources of fluency that can induce a sense of familiarity, as well as cases where fluency is not attributed to memory, among which the phenomenological experience of déjà-vu. Across these two dimensions, we highlight key questions to be answered by future studies to improve our understanding of the underpinnings of this form of memory and contribute to building an integrative neurocognitive model of familiarity. Essential to this aim is the clarification of the computational, cognitive, and neural mechanisms involved, namely global matching, fluency attribution, and sharpening. Furthermore, future research is needed to unravel the relationships between these mechanisms. We argue that to achieve these goals, researchers must use appropriate behavioral paradigms and clearly define which dimension of familiarity they investigate.

FunFuzz : Greybox Fuzzing with Function Significance

Greybox fuzzing is dedicated to revealing software bugs by maximizing code coverage. Concentrating on code coverage, greybox fuzzing effectively exposes bugs in real-world programs by continuously executing the program under test (PUT) with the test inputs generated from initial seeds, making it a popular software testing technique. Although powerful, the effectiveness of greybox fuzzing can be restricted in some cases. Ignoring the significant degrees of executed functions, traditional greybox fuzzing usually fails to identify significant seeds that execute more significant functions, and thus may assign similar energy to significant and trivial seeds when conducting power scheduling. As a result, the effectiveness of greybox fuzzing can be degraded due to wasting too much energy on trivial seeds. In this paper, we introduce function significance (FS) to measure the significant degrees of functions. Our key insight is that the influential functions that connect to many other functions are significant to greybox fuzzing as they provide more probabilities to reach previously unexplored code regions. To quantify FS, we conduct influence analysis upon the call graphs extracted from the PUTs to obtain the centrality values of function nodes. With FS as the significance measurement, we further propose FunFuzz , an FS-aware greybox fuzzing technique, to optimize significant seeds and tackle the aforementioned restriction. To this end, FunFuzz dynamically tracks the functions executed by a seed during fuzzing, and computes the significance score for the seed by accumulating the FS values of the functions executed by it. Based on the computed FS values, FunFuzz then takes an estimation-based power scheduling to assign more (or less) energy to seeds that achieve over-estimated (or under-estimated) significance scores. Specifically, the seed energy is adjusted by multiplying with a scale factor computed regarding the ratio of the actual significance score achieved by executing the seed and the estimated significance score predicted by a linear model constructed on-the-fly. To evaluate FunFuzz , we prototype it on top of AFL++ and conduct experiments with 15 programs, of which 10 are from common real-world projects and five are from Magma, and compare it to seven popular fuzzers. The experimental results obtained through fuzzing exceeding 40,800 CPU hours show that: (1) In terms of covering code, FunFuzz outperforms AFL++ by achieving 0.1% \(\sim\) 18.4% more region coverage on 13 out of 15 targets. (2) In terms of finding bugs, FunFuzz unveils 114 unique crashes and 25 Magma bugs (which are derived from CVEs) in 20 trials of 24-hour fuzzing, which are the most compared to the competitor fuzzers and include 32 crashes and 1 Magma bug that the other fuzzers fail to discover. Besides the experiments focusing on code coverage and bug finding, we evaluate the key components of FunFuzz , namely the FS-centered estimation-based power scheduling and the lazy FS computation mechanism. The extensive evaluation not only suggests FunFuzz ’s superiority in code coverage and bug finding, but also demonstrates the effectiveness of the two components.

Multi-level physics informed deep learning for solving partial differential equations in computational structural mechanics

Physics-informed neural network has emerged as a promising approach for solving partial differential equations. However, it is still a challenge for the computation of structural mechanics problems since it involves solving higher-order partial differential equations as the governing equations are fourth-order nonlinear equations. Here we develop a multi-level physics-informed neural network framework where an aggregation model is developed by combining multiple neural networks, with each one involving only first-order or second-order partial differential equations representing different physics information such as geometrical, constitutive, and equilibrium relations of the structure. The proposed framework demonstrates a remarkable advancement over the classical neural networks in terms of the accuracy and computation time. The proposed method holds the potential to become a promising paradigm for structural mechanics computation and facilitate the intelligent computation of digital twin systems.

An Elastohydrodynamic model of the slot-die coating process

Abstract The slot-die coating process plays an important role in the industry, as it is employed in many different fields. The characteristics of the final application are determined by the flow between the die and the roller. This research paper aims to develop a mathematical model of such flow that takes into account the roller deformations caused by high pressure values reached by the coating fluid. This Elastohydrodynamic model is made up of a coupling between the mathematical model of the flow and the mathematical model of the roller deformations. Model resolution is undertaken numerically by deforming the flow domain according to the roller deformations using computational fluid dynamics (CFD) and computational solid mechanics (CSM) techniques. For its part, the finite volume method (FVM) is used to perform the flow model analysis while the finite element method (FEM) is employed to deal with roller deformations. The results obtained from this model give information on the flow pressure distribution, coating gaps, meniscus position, extent of roller deformations in the coating flow and the influence of different operating conditions. The information obtained from this study is valuable for industrial applications, as it gives insights into the coating process that can help manufacturers to define a suitable combination of operating parameters in order to obtain coating applications that meet quality and performance requirements.

ERLNEIL-MDP: Evolutionary reinforcement learning with novelty-driven exploration for medical data processing

The rapid growth of medical data presents opportunities and challenges for healthcare professionals and researchers. To effectively process and analyze this complex and heterogeneous data, we propose evolutionary reinforcement learning with novelty-driven exploration and imitation learning for medical data processing (ERLNEIL-MDP) algorithm, including a novelty computation mechanism, an adaptive novelty-fitness selection strategy, an imitation-guided experience fusion mechanism, and an adaptive stability preservation module. The novelty computation mechanism quantifies the novelty of each policy based on its dissimilarity to the population and historical data, promoting exploration and diversity. The adaptive novelty-fitness selection strategy balances exploration and exploitation by considering policies' novelty and fitness during selection. The imitation-guided experience fusion mechanism incorporates expert knowledge and demonstrations into the learning process, accelerating the discovery of effective solutions. The adaptive stability preservation module ensures the stability and reliability of the learning process by dynamically adjusting the algorithm's hyperparameters and preserving elite policies across generations. These components work together to enhance the exploration, diversity, and stability of the learning process. The significance of this work lies in its potential to revolutionize medical data analysis, leading to more accurate diagnoses and personalized treatments. Experiments on MIMIC-III and n2c2 datasets demonstrate ERLNEIL-MDP's superior performance, achieving F1 scores of 0.933 and 0.928, respectively, representing 6.0 % and 6.7 % improvements over state-of-the-art methods. The algorithm exhibits strong convergence, high population diversity, and robustness to noise and missing data.

Elastodynamic analysis of rotating solids by novel nodal position finite element method

This study develops a novel 8-node isoparametric hexahedral element using the Nodal Position Finite Element Method (NPFEM) for elastodynamic analysis of rotating solids. The element also incorporates the flexural modes directly into its element shape function to alleviate the shear locking when modeling the bending deformation of solids. Unlike conventional displacement-based finite element methods, which require the decoupling of elastic deformation from rigid-body motions, the NPFEM eliminates this process by directly representing strain and kinetic energies through nodal position coordinates, which avoids potential approximation errors in the decoupling process. To validate the accuracy and efficacy of this new NPFEM solid element, numerical simulations of a beam under static and dynamic loads are conducted and benchmarked against the theoretical solutions. Then, dynamic analysis of a rotating blade demonstrates that the NPFEM element can directly account for the centrifugal stiffening effect and superharmonic resonance of rotating blades without resorting to conventional methods. The successful implementation of this NPFEM element in complex simulations highlights its potential to provide significant advancements in computational mechanics.

The C-V2V-assisted early small cell data offloading (E-SCDO) method using the multi-access edge computing (MEC) mechanism

The C-V2V-assisted early small cell data offloading (E-SCDO) method using the multi-access edge computing (MEC) mechanism

Reversible esterification process in Casson nanofluids: A numerical analysis of non-newtonian hydromagnetic flow

A computational mechanism has been formulated to scientifically analyze the phenomenon of combined convection in the occurrence of magnetohydrodynamic flow behavior of Casson nanofluid. This study emphasizes the trajectory of the nanofluid along a stretching sheet with nonlinear permeability, while considering additional physical effects such as reversible chemical reaction, thermal radiation, energy source/sink, suction and viscous dissipation. In this study, we have additionally integrated the nanofluid paradigm proposed by Buongiorno, which encompasses the repercussion of thermophoresis along with Brownian motion. The utilization of appropriate similarity renovations serves the purpose of recasting the dominant multivariate differential equations to a collection of nonlinear differential equations in single variable. The Runge–Kutta shooting mechanism is implemented for the intention of numerically determining the unknown boundary conditions. The solution to the dominant equations was obtained by employing the fourth-order Runge–Kutta technique and to ensure the dependability of the outcomes, the bvp4c method was employed. The graphical and tabulated observations are presented herein to facilitate a comprehensive analysis of the underlying physical characteristics inherent in the problem at hand. The utilization of the Casson parameter has been found to be advantageous in the reduction of shear stress rate, along with the enhancement of mass and energy transfer rates. Additionally, the application of suction has been observed to be beneficial in the enhancement of Sherwood number and energy transfer rate. Within the scope of the context of the esterification process, the purpose of this proposal is to evaluate the impact that numerous arithmetical values exert on the temperature field, the velocity profile and the volumetric concentration. An in-depth graphical analysis that takes into account the relevant physical consequences is used to evaluate the most important factors that relate to the physical quantities that describe the contours of temperature, velocity and concentration. In addition to being accompanied by their respective explanations, the tabular portrayal of the local Sherwood number, shear stress rate along with local Nusselt number all feature in this paper. There is a clear distinction that can be made between irreversible and reversible flows in the evaluation of the local Sherwood number, rate of shear stress and local Nusselt number. This differentiation is brought about by taking into analysis thermophoresis parameter, Brownian motion parameter and suction parameter. The results that were produced from the theoretical simulations have significant repercussions for a variety of fields that are related to energy engineering. The findings derived from the analysis indicate a negative correlation between the Brownian motion parameter and both irreversible and reversible flow in terms of rate of energy transfer and shear stress rate. It is imperative to highlight that reversible flow holds greater significance in comparison to irreversible flow.

Youths' sensitivity to social media feedback: A computational account.

While it is often argued that continuous exposure to social feedback is specifically challenging for the hypersensitive developing brain, empirical evidence is lacking. Across three studies, we reveal the developmental differences and computational mechanisms that underlie the social media engagement and feedback processing of adolescents and adults. First, using a reinforcement learning model on a large Instagram trace dataset (N = 16,613, 1.6+ million posts), we show that adolescents are more sensitive to social feedback than adults. Second, in an experimental study (N = 194), we show that adolescents' mood is affected more strongly by a reduction in likes than adults. Last, in a neuroimaging study (N = 96), we show that social media feedback sensitivity is related to individual differences in subcortical-limbic brain volumes of emerging adults. Together, these findings highlight the need for digital competence programs to help youth manage the constant feedback they encounter on social media platforms.

Analysis and evaluation of suitability of high-pressure dynamic constitutive model for concrete under blast and impact loading

Concrete demonstrates complex dynamic mechanical behaviors under the influence of blast or impact loads, and there are inherent limitations in experimental and theoretical methods when dealing with highly nonlinear problems. As computational technologies and mechanics continue to evolve, it is possible to conduct high-fidelity simulations of the transient response of concrete structures subjected to intense dynamic loads. Such simulations play a crucial role in revealing the propagation laws of stress waves, the progression of damage, the mechanisms of structural failure, and in conducting protective engineering design. An accurate concrete material model is essential for conducting numerical studies. This article reviews the development of high-pressure dynamic constitutive models for concrete in recent years from both experimental research and theoretical modeling perspectives, focusing on the analysis and evaluation of the modeling methods and main shortcomings of the equation of state(EOS), strength model, and damage model. Using single-element numerical simulations under a single loading path and numerical calculations of engineering cases under complex loading paths, a systematic analysis and comparison were conducted on the predictive capabilities of the HJC, RHT, KCC, and CSC models, as well as the newly developed Kong-Fang and Yan-Chen models. It pointed out the impact of high-pressure mechanical behavior of concrete and the cumulative effect of damage under hydrostatic pressure on the calculation results. Finally, a discussion was conducted on the inherent flaws, applicability, and research difficulties of local constitutive models of concrete in the finite element method. This provides a reference for the selection and research of constitutive models when conducting numerical analysis of the blast and impact resistance of concrete structures.

On the Definition of Velocity in Discrete-Time, Stochastic Langevin Simulations

We systematically develop beneficial and practical velocity measures for accurate and efficient statistical simulations of the Langevin equation with direct applications to computational statistical mechanics and molecular dynamics sampling. Recognizing that the existing velocity measures for the most statistically accurate discrete-time Verlet-type algorithms are inconsistent with the simulated configurational coordinate, we seek to create and analyze new velocity companions that both improve existing methods as well as offer practical options for implementation in existing computer codes. The work is based on the set of GJ methods that, of all methods, for any time step within the stability criteria correctly reproduces the most basic statistical features of a Langevin system; namely correct Boltzmann distribution for harmonic potentials and correct transport in the form of drift and diffusion for linear potentials. Several new and improved velocities exhibiting correct drift are identified, and we expand on an earlier conclusion that, generally, only half-step velocities can exhibit correct, time-step independent Maxwell–Boltzmann distributions. Specific practical and efficient algorithms are given in familiar forms, and these are used to numerically validate the analytically derived expectations. One especially simple algorithm is highlighted, and the ability of one of the new on-site velocities to produce statistically correct averages for a particular damping value is specified.

Image-Based Peridynamic Modeling-Based Micro-CT for Failure Simulation of Composites

By utilizing computed tomography (CT) technology, we can gain a comprehensive understanding of the specific details within the material. When combined with computational mechanics, this approach allows us to predict the structural response through numerical simulation, thereby avoiding the high experimental costs. In this study, the tensile cracking behavior of carbon–silicon carbide (C/SiC) composites is numerically simulated using the bond-based peridynamics model (BB-PD), which is based on geometric models derived from segmented images of three-dimensional (3D) CT data. To obtain results efficiently and accurately, we adopted a deep learning-based image recognition model to identify the kinds of material and then the pixel type that corresponds to the material point, which can be modeled by BB-PD for failure simulation. The numerical simulations of the composites indicate that the proposed image-based peridynamics (IB-PD) model can accurately reconstruct the actual composite microstructure. It can effectively simulate various fracture phenomena such as interfacial debonding, crack propagation affected by defects, and damage to the matrix.

Quantum computing with error mitigation for data-driven computational homogenization

As a crossover frontier of physics and mechanics, quantum computing is showing its great potential in computational mechanics. However, quantum hardware noise remains a critical barrier to achieving accurate simulation results due to the limitation of the current hardware. In this paper, we integrate error-mitigated quantum computing in data-driven computational homogenization, where the zero-noise extrapolation (ZNE) technique is employed to improve the reliability of quantum computing. Specifically, ZNE is utilized to mitigate the quantum hardware noise in two quantum algorithms for distance calculation, namely a Swap-based algorithm and an H-based algorithm, thereby improving the overall accuracy of data-driven computational homogenization. Multiscale simulations of a 2D composite L-shaped beam and a 3D composite cylindrical shell are conducted with the quantum computer simulator Qiskit, and the results validate the effectiveness of the proposed method. We believe this work presents a promising step towards using quantum computing in computational mechanics.

Digital image correlation and infrared thermography data for seven unique geometries of 304L stainless steel

Material Testing 2.0 (MT2.0) is a paradigm that advocates for the use of rich, full-field data, such as from digital image correlation and infrared thermography, for material identification. By employing heterogeneous, multi-axial data in conjunction with sophisticated inverse calibration techniques such as finite element model updating and the virtual fields method, MT2.0 aims to reduce the number of specimens needed for material identification and to increase confidence in the calibration results. To support continued development, improvement, and validation of such inverse methods—specifically for rate-dependent, temperature-dependent, and anisotropic metal plasticity models—we provide here a thorough experimental data set for 304L stainless steel sheet metal. The data set includes full-field displacement, strain, and temperature data for seven unique specimen geometries tested at different strain rates and in different material orientations. Commensurate extensometer strain data from tensile dog bones is provided as well for comparison. We believe this complete data set will be a valuable contribution to the experimental and computational mechanics communities, supporting continued advances in material identification methods.

A Novel Application of Computational Contact Tools on Nonlinear Finite Element Analysis to Predict Ground-Borne Vibrations Generated by Trains in Ballasted Tracks

Predictive numerical models in the study of ground-borne vibrations generated by railway systems have traditionally relied on the subsystem partition approach (segmented). In such a method, loads are individually applied, and the cumulative effect of the rolling stock is obtained through superposition. While this method serves to mitigate computational costs, it may not fully capture the complex interactions involved in ground-borne vibrations—especially in the frequency domain. Recent advancements in computation and software have enabled the development of more sophisticated vibrational contamination prediction models that encompass the entire dynamics of the system, from the rolling stock to the terrain, allowing continuous simulations with a defined time step. Furthermore, the incorporation of computational contact mechanics tools between various elements not only ensures accuracy in the time domain but also extends the analysis into the frequency domain. In this novel approach, the segmented models are shifted to continuous simulations where the nonlinear problem of a rigid–flexible multibody system is fully considered. The model can predict the impact of a high-speed rail (HSR) vehicle passing, capturing the key intricacies of ground-borne vibrations and their impact on the surrounding environment due to a deeper comprehension of the occurrences in the frequency domain.

Neural and Computational Mechanisms of Motivation and Decision-making.

Motivation is often thought to enhance adaptive decision-making by biasing actions toward rewards and away from punishment. Emerging evidence, however, points to a more nuanced view whereby motivation can both enhance and impair different aspects of decision-making. Model-based approaches have gained prominence over the past decade for developing more precise mechanistic explanations for how incentives impact goal-directed behavior. In this Special Focus, we highlight three studies that demonstrate how computational frameworks help decompose decision processes into constituent cognitive components, as well as formalize when and how motivational factors (e.g., monetary rewards) influence specific cognitive processes, decision-making strategies, and self-report measures. Finally, I conclude with a provocative suggestion based on recent advances in the field: that organisms do not merely seek to maximize the expected value of extrinsic incentives. Instead, they may be optimizing decision-making to achieve a desired internal state (e.g., homeostasis, effort, affect). Future investigation into such internal processes will be a fruitful endeavor for unlocking the cognitive, computational, and neural mechanisms of motivated decision-making.

Communicated priors tune the perception of control

Action allows us to shape the world around us. But to act effectively we need to accurately sense what we can and cannot control. Classic theories across cognitive science suppose that this ‘sense of agency’ is constructed from the sensorimotor signals we experience as we interact with our surroundings. But these sensorimotor signals are inherently ambiguous, and can provide us with a distorted picture of what we can and cannot influence. Here we investigate one way that agents like us might overcome the inherent ambiguity of these signals: by combining noisy sensorimotor evidence with prior beliefs about control acquired through explicit communication with others. Using novel tools to measure and model control decisions, we find that explicit beliefs about the controllability of the environment alter both the sensitivity and bias of agentic choices; meaning that we are both better at detecting and more biased to feel control when we are told to expect it. These seemingly paradoxical effects on agentic choices can be captured by a computational model where expecting to be in control exaggerates the sensitivity or ‘gain’ of the mechanisms we use to detect our influence over our surroundings – making us increasingly sensitised to both true and illusory signs of agency. In combination, these results reveal a cognitive and computational mechanism that allows public communication about what we can and cannot influence to reshape our private sense of control.