Data-driven Simulation Research Articles

A New Approach of Data-driven Simulation and Its Application to Solar Active Region 12673

Abstract The solar coronal magnetic field is a pivotal element in the study of eruptive phe-
nomena, and understanding its dynamic evolution has long been a focal point in solar physics.
Numerical models, driven directly by observation data, serve as indispensable tools in investi-
gating the dynamics of the coronal magnetic field. This paper presents a new approach to elec-
tric field inversion, which involves modifying the electric field derived from the DAVE4VM
velocity field using ideal Ohm’s law. The time series of the modified electric field is used as
a boundary condition to drive a full MHD data-driven model, which is applied to simulate
the magnetic field evolution of active region 12673. The simulation results demonstrate that
our method enhances the magnetic energy injection through the bottom boundary, as com-
pared with energy injection calculated directly from the DAVE4VM code, and reproduce of
the evolution of the photospheric magnetic flux. The coronal magnetic field structure is also
in morphological similarity to the coronal loops. This new approach will be applied to the
high-accuracy simulation of eruption phenomena and provide more details on the dynamical
evolution of the coronal magnetic field.

Integrated Workflow Development for Data-Driven Neighborhood-Scale Building Performance Simulation

Abstract As urbanization intensifies, cities are key contributors to energy consumption and carbon emissions, accounting for a significant portion of global energy use and CO2 emissions. This paper introduces a systematic approach to support the development of urban projects with minimized operational carbon footprints through the integration of data-driven building performance simulation (BPS) tools in early-stage design. Emphasizing the necessity for a collaborative effort among designers, policymakers, and other stakeholders, we discuss the evolution of BPS toward incorporating data-driven tools for energy need reduction and informed decision-making. Despite the proliferation of modeling methods and data-related challenges, we present a theoretical workflow, supported by interactions with design firms in the US and European Union (EU) through interviews. This structured approach, demonstrating adaptability and scalability across urban contexts, foregrounds the potential for future data-driven integration in design practices. Grounded in theoretical concepts and preliminary real-world insights, our work emphasizes the transformation of standard activities toward data-driven processes, showcasing the crucial role of practical experience in advancing sustainable, low-carbon urban development.

Data-driven simulation methodology for exploring optimal storage location assignment scheme in warehouses

Storage location assignment effects the efficiency of order picking, and thus it is significant for improving operation management in warehouses. Due to the dynamic nature of order picking, system simulation has been recently used for solving Storage Location Assignment Problem (SLAP). However, most previous studies aimed at using simulation to evaluate and verify the optimum storage schemes obtained from mathematical programming, which belongs to “optimize first and then simulation” in essence. In this context, we proposed a novel data-driven simulation methodology for solving SLAP in this study. A four-quadrant judgment criterion is proposed to guide storage locations assignment based on correlation and turnover frequency of items. Moreover, trajectory and blocked heatmaps are involved for exploring the contribution of different factors to order picking efficiency through scenario experiments. A prototype simulator was then developed and applied in a case study to demonstrate its utility. The results obtained reveals the fact that congestion blocking is another key factor impacting global order picking efficiency, and thus considering the correlation between items in storage schemes designing may not always improve the system performance due to the accompanying increases of blocking intensity during the dynamic order picking processes. The findings also demonstrate our inference that the priority of turnover frequency-based storage policy should be higher than correlation between items in designing storage schemes. This work provides new perspectives and valuable demonstration on the evaluating the contribution of different factors to global order picking efficiency as well as exploring optimal storage schemes through emerging data-driven simulation paradigm.

Evolving Outer Heliosphere: Tracking Solar Wind Transients from 1 au to the VLISM with IBEX and Voyager 1

Interstellar Boundary Explorer (IBEX) observations of energetic neutral atom (ENA) fluxes from the heliosphere have greatly enriched our understanding of the interaction of the solar wind (SW) with the local interstellar medium (LISM). However, there has been recent controversy surrounding the inability of most ENA models to produce as high an intensity of ∼0.5–6 keV ENAs as IBEX observes at 1 au, especially as a function of time. In our previous study (E. J. Zirnstein et al.), we introduced a new model that utilizes a data-driven magnetohydrodynamic simulation of the SW–LISM interaction to propagate pickup ions through the heliosheath (HS) after they are nonadiabatically heated at the heliospheric termination shock. E. J. Zirnstein et al. only simulated and analyzed IBEX observations from the direction of Voyager 2. In this study, we expand our model to include fluxes from the direction of Voyager 1, as well as in the low-latitude part (middle) of the ribbon (10° below the ecliptic plane). We show that the model results at Voyager 1 are consistent with E. J. Zirnstein et al.’s results at Voyager 2 in terms of a secondary ENA source contribution of ≲20% from both directions. Our results in the middle of the ribbon also reproduce the data, when including a time-dependent secondary ENA source. Finally, we demonstrate with our simulation that three large pressure waves likely merged in the VLISM and were observed by Voyager 1 as “pf2,” while at least one of the wave’s effects in the HS was observed by IBEX as a brief enhancement in ENA flux in early 2016.

Data-driven MHD Simulation of the Formation of a Magnetic Flux Rope and an Inclined Solar Eruption

Solar energetic events are caused by the release of magnetic energy accumulated in the solar atmosphere. To understand their initiating physical mechanisms, the dynamics of the coronal magnetic fields must be studied. Unfortunately, the dominant mechanisms are still unclear due to a lack of direct measurements. Numerical simulations based on magnetohydrodynamics (MHD) can reproduce the dynamical evolution of solar coronal magnetic field, providing a useful tool to explore flare initiation. Data-driven MHD simulations, in which the time-series observational data of the photospheric magnetic field is used as the simulation boundary condition, can explore different mechanisms. To investigate the accumulation of free magnetic energy through a solar eruption, we simulated the first of several large flares in NOAA active region 11283. We used a data-driven model that was governed by zero-beta MHD, focusing on the free magnetic energy accumulation prior to the M5.3 flare (2011 September 6 at 01:59 UT). We reproduced the flare-associated eruption following the formation of twisted magnetic fields, or a magnetic flux rope (MFR), formed by photospheric motions at its footpoints. We found that the eruption was first triggered by the growth of the torus instability. The erupting MFR caused magnetic reconnections with neighboring magnetic field lines located along the direction of the eruption. Using the simulation results and an axial-radial decay index centered on the MFR, we find a natural explanation for the inclination of the eruption and a possible approach to predict the direction of solar eruptive events.

Data-driven Simulation of Effects of a Solar Flare with Extreme-ultraviolet Late Phase on Ionospheric Electron Density

Effects of the extreme-ultraviolet (EUV) late phase of solar flares on the ionosphere were rarely studied. Here we simulated such effects on the ionospheric electron density using an ionosphere−thermosphere coupled model driven by the realistic solar spectrum observed during the X1.8 flare on 2012 October 23. Global total electron content (TEC) observations and simulations showed that the dayside ionospheric TEC during the EUV late phase increased more than that of the flare’s main phase. We examined the performance of the model for flares with EUV late phase. The results showed that the F-region electron density enhancement and recovery did not vary in the same pace as the temporal variations of the EUV late phase, and the presence of the EUV late phase prolonged the recovery of electron density by ∼9 hr. We also found that the enhancement in electron density was mainly determined by the chemical production, while the recovery of electron density was primarily controlled by the electric field transport effects. This study enhanced understanding of the intricate physical and photochemical processes governing Earth’s space environment and similar planetary systems during solar flare events with EUV late phase.

Extra trees regression assisted 1D monolith reactor simulations based on microkinetic analysis and rate transformation

The incorporation of microkinetic method for surface reaction calculation is a key development direction in monolith reactor simulations, albeit posing significant computational stability and efficiency challenges. A data-driven multi-scale simulation framework is proposed to bridge the gap between macroscopic numerical model and microscopic reaction solvers. After the generating of precomputed microkinetic dataset, we utilize a tailor-designed preprocessing transformation, AD_log10, to effectively manage the common challenges of large variances and reversed directions in microkinetic rate data. A suite of machine learning regression models is developed as reaction rate solver integrated into the 1D two-phase monolith model. We employ NH3 selective catalytic oxidation on Cu(100) and Cu(111) as probe systems to validate our framework’s efficacy. In the context of on-board simulations under operating conditions, the Extra Trees Regression (with best regression performance) based model exhibits commendable accuracy and efficiency, successfully addressing complex processes previously unmanageable with interpolation-based models. This innovative approach paves the way for advanced monolith reactor modeling and sheds light on multi-scale simulation methodologies in chemical reaction engineering.

Measuring receptivity to misinformation at scale on a social media platform.

Measuring the impact of online misinformation is challenging. Traditional measures, such as user views or shares on social media, are incomplete because not everyone who is exposed to misinformation is equally likely to believe it. To address this issue, we developed a method that combines survey data with observational Twitter data to probabilistically estimate the number of users both exposed to and likely to believe a specific news story. As a proof of concept, we applied this method to 139 viral news articles and find that although false news reaches an audience with diverse political views, users who are both exposed and receptive to believing false news tend to have more extreme ideologies. These receptive users are also more likely to encounter misinformation earlier than those who are unlikely to believe it. This mismatch between overall user exposure and receptive user exposure underscores the limitation of relying solely on exposure or interaction data to measure the impact of misinformation, as well as the challenge of implementing effective interventions. To demonstrate how our approach can address this challenge, we then conducted data-driven simulations of common interventions used by social media platforms. We find that these interventions are only modestly effective at reducing exposure among users likely to believe misinformation, and their effectiveness quickly diminishes unless implemented soon after misinformation's initial spread. Our paper provides a more precise estimate of misinformation's impact by focusing on the exposure of users likely to believe it, offering insights for effective mitigation strategies on social media.

Photospheric Pore Rotation Associated with a C-class Flare from Spectropolarimetric Observations with DKIST

We present high-resolution observations of a C4.1-class solar flare (SOL2023-05-03T20:53) in AR 13293 from the Visible Spectro-Polarimeter (ViSP) and Visible Broadband Imager (VBI) instruments at the DKIST. The fast cadence, good resolution, and high polarimetric sensitivity of ViSP data provide a unique opportunity to explore the photospheric magnetic fields before and during the flare. We infer the magnetic field vector in the photosphere from the Fe i 6302 Å line using Milne–Eddington inversions. Combined analysis of the inverted data and VBI images reveals the presence of two opposite polarity pores exhibiting rotational motion both prior to and throughout the flare event. Data-driven simulations further reveal a complex magnetic field topology above the rotating pores, including a null-point-like configuration. We observed a 30% relative change in the horizontal component (δ F h ) of Lorentz force at the flare peak time and roughly no change in the radial component. We find that the changes in δ F h are the most likely driver of the observed pore rotation. These findings collectively suggest that the back reaction of magnetic field line reconfiguration in the corona may influence the magnetic morphology and rotation of pores in the photosphere on a significantly smaller scale.

Data-driven vulnerability analysis of shared electric vehicle systems to cyberattacks

Disruptions to electric vehicle charging infrastructure can hinder consumers’ confidence and impact the performance of shared electric vehicle (SEV) systems. This study focuses on SEV systems’ vulnerability to emerging cyberattacks at the system level utilizing city-scale SEV trajectory data in Beijing, China. A data-driven simulation model is developed to account for realistic charging demand–supply interactions extracted from the data. The results show that among different types of disruptions, cybersecurity threats present unique challenges to SEV systems. Compared with attacking the most highly utilized charging stations, attacking popular ones on the city’s outskirts is more likely to significantly increase stress on the charging infrastructure and reduce the accessibility of SEVs. Meanwhile, the interactions between SEVs and infrastructure could either amplify the impacts by triggering cascading failures or relieve the adversarial effects. These findings provide insights into designing policies and solutions to enhance the resilience of SEV systems against cyberattacks.

Filter Cake Neural-Objective Data Modeling and Image Optimization

Designing drilling mud rheology is a complex task, particularly when it comes to preventing filter cakes from obstructing formation pores and making sure they can be easily decomposed using breakers. Incorporating both multiphysics and data-driven numerical simulations into the design of mud rheology experiments creates an additional challenge due to their symmetrical integration. In this computational intelligence study, we introduced numerical validation techniques using 498 available datasets from mud rheology and images from filter cakes. The goal was to symmetrically predict flow, maximize filtration volume, monitor void spaces, and evaluate formation damage occurrences. A neural-objective and image optimization approach to drilling mud rheology automation was employed using an artificial neural network feedforward (ANN-FF) function, a non-ANN-FF function, an image processing tool, and an objective optimization tool. These methods utilized the Google TensorFlow Sequential API-DNN architecture, MATLAB-nftool, the MATLAB-image processing tool, and a single-objective optimization algorithm. However, the analysis emanating from the ANN-FF and non-ANN-FF (with neurons of 10, 12, and 18) indicated that, unlike non-ANN-FF, ANN-FF obtained the highest correlation coefficient of 0.96–0.99. Also, the analysis of SBM and OBM image processing revealed a total void area of 1790 M µm2 and 1739 M µm2, respectively. Both SBM and OBM exhibited notable porosity and permeability that contributed to the enhancement of the flow index. Nonetheless, this study did reveal that the experimental-informed single objective analysis impeded the filtration volume; hence, it demonstrated potential formation damage. It is, therefore, consistent to note that automating flow predictions from mud rheology and filter cakes present an alternative intelligence method for non-programmers to optimize drilling productive time.

Discrete-event simulation to analyse the impacts of a reduced minimum order quantity in the food industry: proposing a shared-savings contract

ABSTRACT Retail partners of the food processing industry benefit from lower minimum order quantities, as they are more responsive to changing market conditions and reduce inventory costs. However, production systems in the food processing industry focus on economies of scale by producing large volumes. A shared-savings contract helps to find a trade-off between the two parties, where the retailer shares a part of the cost savings with the manufacturer to cope with higher production costs. We propose a generic data-driven discrete-event simulation model to guide decision-making in the development of a shared-savings contract. Thereby, we examined breakdown and rework behaviours after setups with a Big Data analysis and a correlation analysis. We aim to evaluate the cost development at a cheese processing company, resulting from a reduced minimum order quantity. Our findings revealed that production costs significantly increase with higher breakdowns, setups, and rework. A 20% increase in orders, resulting from a reduced minimum order quantity, requires an additional production capacity of 3%. To conclude, transparent information sharing is key to developing a shared-savings contract in the food supply chain.

Pressure-Sensitive-Paint-Driven Hybrid Unsteady Compressible Flow Simulation

A hybrid unsteady compressible computational fluid dynamics simulation (CFD) driven by pressure-sensitive paint (PSP) data is proposed, and the concept is demonstrated in two dimensions. By imposing the wall pressure acquired with PSP as the Dirichlet boundary condition, an unsteady compressible Euler equation solver is marched together with integral boundary-layer equations in time. As a result, an entire flowfield that fits the PSP measurement is created in the CFD domain. This concept is tested by taking PSP data from a past article of a wind-tunnel test using the NASA CRM65 airfoil and the steady and unsteady capabilities of this data-driven hybrid simulation are demonstrated for transonic flows in two dimensions, and the convergence characteristics are also investigated. It is found that a steady shock position, which cannot be accurately predicted by the Euler equation solver with a boundary layer, can be rectified by the hybrid simulation. Moreover, unsteady shock motion due to buffeting can also be captured well by the hybrid simulation.

MBD-NODE: physics-informed data-driven modeling and simulation of constrained multibody systems

MBD-NODE: physics-informed data-driven modeling and simulation of constrained multibody systems

Adaptive Location-based Routing Protocols for Dynamic Wireless Sensor Networks in Urban Cyber-physical Systems

This study investigates the development and enhancement of adaptive location-based routing protocols within dynamic wireless sensor networks (WSNs) in urban cyber-physical systems, recommending the implementation of the study’s innovative Urban Adaptive Location-based Routing Protocol (UALRP). This innovative protocol integrates real-time data analytics and adaptive machine learning models into its algorithmic framework to dynamically optimize routing decisions based on continuously changing urban conditions. Through the utilization of data-driven simulation models and machine learning techniques, the research sought to significantly improve the efficiency, reliability, and scalability of urban WSNs. Existing protocols such as Geographic Adaptive Fidelity (GAF), Greedy Perimeter Stateless Routing (GPSR), and Dynamic Source Routing (DSR) were critically assessed under urban settings using extensive datasets detailing New York City's traffic patterns and environmental variables. The analysis demonstrated that while GPSR showed superior performance in terms of latency, throughput, and energy efficiency among the traditional protocols, the introduction of UALRP, with its advanced predictive and adaptive capabilities, can further optimize these metrics. The study affirms the critical role of enhancing location accuracy and the ongoing advancement of machine learning models within urban routing protocols. These insights advocate for the broader implementation of adaptive strategies like UALRP to foster the development of more resilient and efficient urban cyber-physical systems.

Predicting the Population Health Economic Impact of Current and New Cancer Treatments for Colorectal Cancer: A Data-Driven Whole Disease Simulation Model for Predicting the Number of Patients with Colorectal Cancer by Stage and Treatment Line in Australia

ObjectivesEffective healthcare planning, resource allocation, and budgeting require accurate predictions of the number of patients needing treatment at specific cancer stages and treatment lines. The Predicting the Population Health Economic Impact of Current and New Cancer Treatments (PRIMCAT) for Colorectal Cancer (CRC) simulation model (PRIMCAT-CRC) was developed to meet this requirement for all CRC stages and relevant molecular profiles in Australia. MethodsReal-world data were used to estimate treatment utilization and time-to-event distributions. This populated a discrete-event simulation, projecting the number of patients receiving treatment across all disease stages and treatment lines for CRC and forecasting the number of patients likely to utilize future treatments. Illustrative analyses were undertaken, estimating treatments across disease stages and treatment lines over a 5-year period (2022-2026). We demonstrated the model’s applicability through a case study introducing pembrolizumab as a first-line treatment for mismatch-repair-deficient stage IV. ResultsClinical registry data from 7163 patients informed the model. The model forecasts 15 738 incident and 2821 prevalent cases requiring treatment in 2022, rising to 15 921 and 2871, respectively, by 2026. Projections show that over 2022 to 2026, there will be a total of 116 752 treatments initiated, with 43% intended for stage IV disease. The introduction of pembrolizumab is projected for 706 patients annually, totaling 3530 individuals starting treatment with pembrolizumab over the forecasted period, without significantly altering downstream utilization of subsequent treatments. ConclusionsPRIMCAT-CRC is a versatile tool that can be used to estimate the eligible patient populations for novel cancer therapies, thereby reducing uncertainty for policymakers in decisions to publicly reimburse new treatments.

Automatic adjoint-based inversion schemes for geodynamics: reconstructing the evolution of Earth's mantle in space and time

Abstract. Reconstructing the thermo-chemical evolution of Earth's mantle and its diverse surface manifestations is a widely recognised grand challenge for the geosciences. It requires the creation of a digital twin: a digital representation of Earth's mantle across space and time that is compatible with available observational constraints on the mantle's structure, dynamics and evolution. This has led geodynamicists to explore adjoint-based approaches that reformulate mantle convection modelling as an inverse problem, in which unknown model parameters can be optimised to fit available observational data. Whilst there has been a notable increase in the use of adjoint-based methods in geodynamics, the theoretical and practical challenges of deriving, implementing and validating adjoint systems for large-scale, non-linear, time-dependent problems, such as global mantle flow, has hindered their broader use. Here, we present the Geoscientific ADjoint Optimisation PlaTform (G-ADOPT), an advanced computational modelling framework that overcomes these challenges for coupled, non-linear, time-dependent systems by integrating three main components: (i) Firedrake, an automated system for the solution of partial differential equations using the finite-element method; (ii) Dolfin-Adjoint, which automatically generates discrete adjoint models in a form compatible with Firedrake; and (iii) the Rapid Optimisation Library, ROL, an efficient large-scale optimisation toolkit; G-ADOPT enables the application of adjoint methods across geophysical continua, showcased herein for geodynamics. Through two sets of synthetic experiments, we demonstrate the application of this framework to the initial condition problem of mantle convection, in both square and annular geometries, for both isoviscous and non-linear rheologies. We confirm the validity of the gradient computations underpinning the adjoint approach, for all cases, through second-order Taylor remainder convergence tests and subsequently demonstrate excellent recovery of the unknown initial conditions. Moreover, we show that the framework achieves theoretical computational efficiency. Taken together, this confirms the suitability of G-ADOPT for reconstructing the evolution of Earth's mantle in space and time. The framework overcomes the significant theoretical and practical challenges of generating adjoint models and will allow the community to move from idealised forward models to data-driven simulations that rigorously account for observational constraints and their uncertainties using an inverse approach.

A Data-Driven Pandemic Simulator with Reinforcement Learning

After the coronavirus disease 2019 (COVID-19) outbreak erupted, it swiftly spread globally and triggered a severe public health crisis in 2019. To contain the virus’s spread, several countries implemented various lockdown measures. As the governments faced this unprecedented challenge, understanding the impact of lockdown policies became paramount. The goal of addressing the pandemic crisis is to devise prudent policies that strike a balance between safeguarding lives and maintaining economic stability. Traditional mathematical and statistical models for studying virus transmission only offer macro-level predictions of epidemic development and often overlook individual variations’ impact, therefore failing to reflect the role of government decisions. To address this challenge, we propose an integrated framework that combines agent-based modeling (ABM) and deep Q-network (DQN) techniques. This framework enables a more comprehensive analysis and optimization of epidemic control strategies while considering real human behavior. We construct a pandemic simulator based on the ABM method, accurately simulating agents’ daily activities, interactions, and the dynamic spread of the virus. Additionally, we employ a data-driven approach and adjust the model through real statistical data to enhance its effectiveness. Subsequently, we integrated ABM into a decision-making framework using reinforcement learning techniques to explore the most effective strategies. In experiments, we validated the model’s effectiveness by simulating virus transmission across different countries globally. In this model, we obtained decision outcomes when governments focused on various factors. Our research findings indicate that our model serves as a valuable tool for decision-makers, enabling them to formulate prudent and rational policies.

Deciphering the dynamical chromosome structural reorganizations in human neural development

Understanding the mechanisms of cell-fate determination in neural development is pivotal for advancing regenerative medicine and addressing neurodegenerative diseases. Cell-fate determination is controlled by the underlying gene expression networks, which are further regulated by the three-dimensional chromosome structures. During neural development, chromosomes progressively adapt their structures to accommodate the requisite gene expressions. However, elucidating the pathways of chromosome structural dynamics during these transitions remains a grand challenge. In this study, we employed the data-driven coarse-grained molecular dynamics simulations, coupled with the nonequilibrium landscape-switching model, to quantify the chromosome structural dynamics during human neural development. We focused on a simplified human neural developmental system, comprising of cell differentiation, reprogramming, and transdifferentiation among the neural progenitor cell (NPC), the glia cell, and the neuron cell. We identified significant large-scale chromosome structural reorganizations during cell-state transitions. From the chromosome structural perspective, the transdifferentiation processes between the glia and neuron cells exhibited nonmonotonic behaviors characterized by an initial increase followed by a subsequent decrease in cell stemness. The transdifferentiation appeared to share the same routes of differentiation after passing through the NPC. Additionally, our findings revealed that the chromosome structural dynamical pathways at the scale of topologically associating domains exhibited little overlap, in contrast to the ones at the long-range regions. This suggests that the active, ATP-driven molecular processes play dominant roles in modulating topologically associating domain (TAD) structures, while the compartmental segregation in chromosomes is primarily governed by the passive phase separation. Our study offers a theoretical exploration of neural cell-fate determination from the chromosome structural perspective, paving a way for potential applications in neuroregeneration. Published by the American Physical Society 2024

Learning Soft Millirobot Multimodal Locomotion with Sim-to-Real Transfer.

With wireless multimodal locomotion capabilities, magnetic soft millirobots have emerged as potential minimally invasive medical robotic platforms. Due to their diverse shape programming capability, they can generate various locomotion modes, and their locomotion can be adapted to different environments by controlling the external magnetic field signal. Existing adaptation methods, however, are based on hand-tuned signals. Here, a learning-based adaptive magnetic soft millirobot multimodal locomotion framework empowered by sim-to-real transfer is presented. Developing a data-driven magnetic soft millirobot simulation environment, the periodic magnetic actuation signal is learned for a given soft millirobot in simulation. Then, the learned locomotion strategy is deployed to the real world using Bayesian optimization and Gaussian processes. Finally, automated domain recognition and locomotion adaptation for unknown environments using a Kullback-Leibler divergence-based probabilistic method are illustrated. This method can enable soft millirobot locomotion to quickly and continuously adapt to environmental changes and explore the actuation space for unanticipated solutions with minimum experimental cost.