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A novel index-based method (RIVA) for the assessment of intrinsic groundwater vulnerability is proposed, based on the successful concept of the European approach (Zwahlen 2003) and by incorporating additional elements that provide more realistic and representative results. Its concept includes four main factors: accounting for the recharge to the system (R), the infiltration conditions (I), the protection offered by the vadose zone (V), and the aquifer characteristics (A). Several sub-factors and parameters are involved in calculation of the final intrinsic vulnerability index. However, even though RIVA is a comprehensive method that produces reliable results, it is not data intensive, does not require advanced skills in data preparation and processing, and may safely be applied regardless of aquifer type, prevalent porosity, geometric and geo-tectonic setup, and site-specific conditions. Its development has incorporated careful consideration of all key existing groundwater vulnerability methods and their critical aspects (factors, parameters, rating, etc.). It has studied and endorsed their virtues while avoiding or modifying factors and approaches that are either difficult to quantify, ambiguous to assess, or non-uniformly applicable to every hydrogeological setup. RIVA has been successfully demonstrated in the intensively cultivated area of Kopaida plain, Central Greece, which is characterized by a complex and heterogeneous geological background. Its validation was performed by a joint compilation of ground-truth monitoring-based data, in-depth knowledge of the geological structure, hydrogeological setup, regional hydrodynamic evolution mechanisms, and also by the dominant driving pressures. Results of the performed validation clearly demonstrated the validity of the proposed methodology to capture the spatially distributed zones of different vulnerability classes accurately and reliably, as these are shaped by the considered factors. RIVA method proved that it may be safely considered to be a fair trade-off between succeeded accuracy, and data intensity and investment to reach highly accurate results. As such, it is envisaged to become an efficient method of performing reliable groundwater vulnerability assessments of complex environments when neither resources occur nor time to generate intensive data is available, and ultimately be valorized for further risk assessment and decision-making processes related to groundwater resource management.

We present Korali, an open-source framework for large-scale Bayesian uncertainty quantification and stochastic optimization. The framework relies on non-intrusive sampling of complex multiphysics models and enables their exploitation for optimization and decision-making. In addition, its distributed sampling engine makes efficient use of massively-parallel architectures while introducing novel fault tolerance and load balancing mechanisms. We demonstrate these features by interfacing Korali with existing high-performance software such as Aphros, LAMMPS (CPU-based), and Mirheo (GPU-based) and show efficient scaling for up to 512 nodes of the CSCS Piz Daint supercomputer. Finally, we present benchmarks demonstrating that Korali outperforms related state-of-the-art software frameworks., Computer Methods in Applied Mechanics and Engineering, 389, ISSN:0045-7825, ISSN:1879-2138

Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables to provide equipment state monitoring services and to generate decision-making for equipment operations. In this paper, we propose two machine learning models: 1) to forecast the amount of pentane (C5) content in the final product mixture; 2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach by using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models.

Supply chain agility and resilience are key factors for the success of manufacturing companies in their attempt to respond to dynamic changes. Circular economy, the need for optimized material flows, ad-hoc responses and personalization are some of the trends that require supply chains to become “cognitive”, i.e. able to predict trends and flexible enough in dynamic environments, ensuring optimized operational performance. Digital Twins (DTs) is a promising technology, and a lot of work is done on the factory level. In this paper, the concept of Cognitive Digital Twins (CDTs) and how they can be deployed in connected and agile supply chains is elaborated. The need for CDTs in the supply chain as well as the main CDT enablers and how they can be deployed under an operational model in agile networks is described. Emphasis is given on the modelling, cognition and governance aspects as well as on how a supply chain can be configured as a network of connected CDTs. Finally, a deployment methodology of the developed model into an example of a circular supply chain is proposed.

Simulations of blood flows in microfluidic devices and physiological systems are gaining importance in complementing experimental and clinical studies. The predictive capabilities of these simulations hinge on the parameters of the red blood cell (RBC) model that are usually calibrated from experimental data. However, these parameter values may vary drastically when calibrated using different experimental quantities or experimental settings. In turn, the results of existing blood flow simulations largely depend on the utilized parameters that have been chosen to validate a particular experiment. We suggest a revision to this type of model calibration to properly integrate experimental data in the computational models and accordingly inform their predictions. In this context, we introduce the calibration of a popular RBC model using data-driven, hierarchical Bayesian inference. We employ data from classical experiments of RBC stretching by optical tweezers and tank treading in shear flows, and distinguish the calibration of the model parameters through single-level and hierarchical Bayesian uncertainty quantification. We find that the optimal model parameters depend not only on the data used for the inference but also on the way the data are used in the inference process. Single-level Bayesian models predict well the data used in their calibration, but are inferior to the hierarchical Bayesian model at predicting previously unseen data. This work demonstrates that the proper integration of experimental data is essential for the development of a robust and transferable RBC model. We believe that the present study can serve as a prototype across scientific fields, in revising the integration of computational models and heterogeneous experimental data. © 2021 American Physical Society ISSN:2331-7019

Effective intervention strategies for epidemics rely on the identification of their origin and on the robustness of the predictions made by network disease models. We introduce a Bayesian uncertainty quantification framework to infer model parameters for a disease spreading on a network of communities from limited, noisy observations; the state-of-the-art computational framework compensates for the model complexity by exploiting massively parallel computing architectures. Using noisy, synthetic data, we show the potential of the approach to perform robust model fitting and additionally demonstrate that we can effectively identify the disease origin via Bayesian model selection. As disease-related data are increasingly available, the proposed framework has broad practical relevance for the prediction and management of epidemics., Royal Society Open Science, 8 (1), ISSN:2054-5703

The systematic identification of infected individuals is critical for the containment of the COVID-19 pandemic. Currently, the spread of the disease is mostly quantified by the reported numbers of infections, hospitalisations, recoveries and deaths; these quantities inform epidemiology models that provide forecasts for the spread of the epidemic and guide policy making. The veracity of these forecasts depends on the discrepancy between the numbers of reported, and unreported yet infectious, individuals. We combine Bayesian experimental design with an epidemiology model and propose a methodology for the optimal allocation of limited testing resources in space and time, which maximises the information gain for such unreported infections. The proposed approach is applicable at the onset and spread of the epidemic and can forewarn of a possible recurrence of the disease after relaxation of interventions. We examine its application in Switzerland; the open source software is, however, readily adaptable to countries around the world. We find that following the proposed methodology can lead to vastly less uncertain predictions for the spread of the disease, thus improving estimates of the effective reproduction number and the future number of unreported infections. This information can provide timely and systematic guidance for the effective identification of infectious individuals and for decision-making regarding lockdown measures and the distribution of vaccines., Swiss Medical Weekly, 150, ISSN:1424-7860, ISSN:1424-3997

Predictive simulations of complex systems are essential for applications ranging from weather forecasting to drug design. The veracity of these predictions hinges on their capacity to capture the effective system dynamics. Massively parallel simulations predict the system dynamics by resolving all spatiotemporal scales, often at a cost that prevents experimentation while their findings may not allow for generalisation. On the other hand reduced order models are fast but limited by the frequently adopted linearization of the system dynamics and/or the utilization of heuristic closures. Here we present a novel systematic framework that bridges large scale simulations and reduced order models to Learn the Effective Dynamics (LED) of diverse complex systems. The framework forms algorithmic alloys between non-linear machine learning algorithms and the Equation-Free approach for modeling complex systems. LED deploys autoencoders to formulate a mapping between fine and coarse-grained representations and evolves the latent space dynamics using recurrent neural networks. The algorithm is validated on benchmark problems and we find that it outperforms state of the art reduced order models in terms of predictability and large scale simulations in terms of cost. LED is applicable to systems ranging from chemistry to fluid mechanics and reduces the computational effort by up to two orders of magnitude while maintaining the prediction accuracy of the full system dynamics. We argue that LED provides a novel potent modality for the accurate prediction of complex systems., 39 pages (Appendix included)

The reproduction number (R0) is broadly considered as a key indicator for the spreading of the COVID-19 pandemic. The estimation of its value with respect to the key threshold of 1.0 is a measure of the need, and eventually effectiveness, of interventions imposed in various countries. Here we present an online tool for the data driven inference and quantification of uncertainties for R0 as well as the time points of interventions for 51 European countries. The study relies on the Bayesian calibration of the simple and well established SIR model with data from reported daily infections. The model is able to fit the data for most countries without individual tuning of parameters. We deploy an open source Bayesian inference framework and efficient sampling algorithms to present a publicly available GUI (https://www.cse-lab.ethz.ch/coronavirus/) that allows the user to assess custom data and compare predictions for pairs of European countries. The results provide a ranking based on the rate of the disease's spread suggesting a metric for the effectiveness of social distancing measures. They also serve to demonstrate how geographic proximity and related times of interventions can lead to similarities in the progression of the epidemic.

We propose the S-leaping algorithm for the acceleration of Gillespie’s stochastic simulation algorithm that combines the advantages of the two main accelerated methods; the $$\tau $$ -leaping and R-leaping algorithms. These algorithms are known to be efficient under different conditions; the $$\tau $$ -leaping is efficient for non-stiff systems or systems with partial equilibrium, while the R-leaping performs better in stiff system thanks to an efficient sampling procedure. However, even a small change in a system’s set up can critically affect the nature of the simulated system and thus reduce the efficiency of an accelerated algorithm. The proposed algorithm combines the efficient time step selection from the $$\tau $$ -leaping with the effective sampling procedure from the R-leaping algorithm. The S-leaping is shown to maintain its efficiency under different conditions and in the case of large and stiff systems or systems with fast dynamics, the S-leaping outperforms both methods. We demonstrate the performance and the accuracy of the S-leaping in comparison with the $$\tau $$ -leaping and R-leaping on a number of benchmark systems involving biological reaction networks.

Background: The diagnostic assessment of fetal arrhythmias relies on the measurements of atrioventricular (AV) and ventriculoatrial (VA) time intervals. Pulsed Doppler over in- and outflow of the left ventricle and tissue Doppler imaging are well-described methods, while Doppler measurements between the left brachiocephalic vein and the aortic arch are less investigated. The aim of this study was to compare these methods of measurement, to find influencing factors on AV and VA times and their ratio, and to create reference ranges. Methods: Echocardiography was performed between 16 and 40 weeks of gestation in normal singleton pregnancies. Nomograms for the individual measurements were created using quantile regression with Matlab Data Analytics. Statistical analyses were performed with GraphPad version 5.0 for Windows. Results: A total of 329 pregnant women were enrolled. A significant correlation exists between AV and VA times and gestational age (GA) (p = 0.0104 to p = 0.08 to 0.60). All measurements differed significantly amongst the studied methods (p < 0.0001). Conclusions: AV and VA intervals increase proportionally with GA; no other independent influencing factors could be identified. As significant differences exist between the three methods of assessment, it is crucial to use appropriate reference ranges to diagnose pathologies.

Natural swimmers rely for their survival on sensors that gather information from the environment and guide their actions. The spatial organization of these sensors, such as the visual fish system and lateral line, suggests evolutionary selection, but their optimality remains an open question. Here, we identify sensor configurations that enable swimmers to maximize the information gathered from their surrounding flow field. We examine two-dimensional, self-propelled and stationary swimmers that are exposed to disturbances generated by oscillating, rotating and D-shaped cylinders. We combine simulations of the Navier–Stokes equations with Bayesian experimental design to determine the optimal arrangements of shear and pressure sensors that best identify the locations of the disturbance-generating sources. We find a marked tendency for shear stress sensors to be located in the head and the tail of the swimmer, while they are absent from the midsection. In turn, we find a high density of pressure sensors in the head along with a uniform distribution along the entire body. The resulting optimal sensor arrangements resemble neuromast distributions observed in fish and provide evidence for optimality in sensor distribution for natural swimmers. ISSN:0022-1120 ISSN:1469-7645

Molecular simulations are one of the most prominent discovery tools in science and engineering, widely adopted in applications ranging from drug discovery to materials design. The fidelity of the predictions of molecular simulations hinges on the reliability and accuracy of the chosen force fields. Thus, the quantification of uncertainties associated with the form of force fields and their parameters is a fundamental part of molecular modeling. This aspect has been rather overlooked in the past but constitutes today an active field of research. This review focuses on the data-driven Bayesian calibration of force fields. The calibration of molecular force fields is a very challenging task in the presence of an ever-increasing amount, type, and scale of reference data. Bayesian data analysis provides an invaluable tool to address this challenge, notably in the presence of model inadequacy. It enables a rigorous definition of uncertainties for the force fields and allows for data-driven robust predictions through molecular simulations. This chapter reviews recent methodological and algorithmic developments in Bayesian calibration of force fields and their application to atomic and molecular fluids. We discuss Bayesian approaches and their computational challenges followed by a description of target applications. We believe that the advances in Bayesian inference presented in this chapter would be valuable for a number of other fields in science and engineering that are revolutionized by data science.

Fish schooling implies an awareness of the swimmers for their companions. In flow mediated environments, in addition to visual cues, pressure and shear sensors on the fish body are critical for providing quantitative information that assists the quantification of proximity to other swimmers. Here we examine the distribution of sensors on the surface of an artificial swimmer so that it can optimally identify a leading group of swimmers. We employ Bayesian experimental design coupled with two-dimensional Navier Stokes equations for multiple self-propelled swimmers. The follower tracks the school using information from its own surface pressure and shear stress. We demonstrate that the optimal sensor distribution of the follower is qualitatively similar to the distribution of neuromasts on fish. Our results show that it is possible to identify accurately the center of mass and even the number of the leading swimmers using surface only information.

We present the algorithm CCMA-ES, an extension to CMA-ES, an evolution strategy that has shown to perform well in a broad range of black-box optimization problems. The (μ, λ)-CMA-ES effectively handles nonlinear nonconvex functions but faces difficulties in constrained optimization problems. We introduce viability boundaries to improve the search for an initial point in the valid domain and adapt the covariance matrix using normal approximations to maintain the inequality constraints. Using benchmark problems from 2006 CEC we compare the performance of CCMA-ES with a state of the art optimization algorithm (mViE) showing favorable results. Finally, CCMA-ES is applied to a pharmacodynamics problem describing tumor growth, and we demonstrate that CCMA-ES outperforms mViE in terms of the objective function value and total function evaluations.

We investigate the capacity of tendons to bear substantial loads by exploiting their hierarchical structure and the viscous nature of their subunits. We model and analyze two successive tendon scales: the fibril and fiber subunits. We present a novel method for bridging intra-scale experimental observations by combining a homogenization analysis technique with a Bayesian inference method. This allows us to infer elastic and viscoelastic moduli at the embedded fibril scale that are mechanically compatible with the experimental data observed at the fiber scale. We identify the rather narrow range of moduli values at the fibrillar scale that can reproduce the mechanical behavior of the fiber, while we quantify the viscoelastic contribution of the embedding, non-collagenous matrix substance. The computed viscoelastic moduli suggest that a great part of the stress relaxation capacity of tendons needs to be attributed to the embedding matrix substance of its inner components, classifying it as a primal load relaxation constituent.

The necessity for accurate and computationally efficient representations of water in atomistic simulations that can span biologically relevant timescales has born the necessity of coarse-grained (CG) modeling. Despite numerous advances, CG water models rely mostly on a-priori specified assumptions. How these assumptions affect the model accuracy, efficiency, and in particular transferability, has not been systematically investigated. Here we propose a data driven comparison and selection for CG water models through a Hierarchical Bayesian framework. We examine CG water models that differ in their level of coarse-graining, structure, and number of interaction sites. We find that the importance of electrostatic interactions for the physical system under consideration is a dominant criterion for the model selection. Multi-site models are favored, unless the effects of water in electrostatic screening are not relevant, in which case the single site model is preferred due to its computational savings. The charge distribution is found to play an important role in the multi-site model’s accuracy while the flexibility of the bonds/angles may only slightly improve the models. Furthermore, we find significant variations in the computational cost of these models. We present a data informed rationale for the selection of CG water models and provide guidance for future water model designs.

We propose the S-leaping algorithm for the acceleration of Gillespie's stochastic simulation algorithm that combines the advantages of the two main accelerated methods; the [Formula: see text]-leaping and R-leaping algorithms. These algorithms are known to be efficient under different conditions; the [Formula: see text]-leaping is efficient for non-stiff systems or systems with partial equilibrium, while the R-leaping performs better in stiff system thanks to an efficient sampling procedure. However, even a small change in a system's set up can critically affect the nature of the simulated system and thus reduce the efficiency of an accelerated algorithm. The proposed algorithm combines the efficient time step selection from the [Formula: see text]-leaping with the effective sampling procedure from the R-leaping algorithm. The S-leaping is shown to maintain its efficiency under different conditions and in the case of large and stiff systems or systems with fast dynamics, the S-leaping outperforms both methods. We demonstrate the performance and the accuracy of the S-leaping in comparison with the [Formula: see text]-leaping and R-leaping on a number of benchmark systems involving biological reaction networks.

The Lennard-Jones (LJ) potential is a cornerstone of Molecular Dynamics (MD) simulations and among the most widely used computational kernels in science. The LJ potential models atomistic attraction and repulsion with century old prescribed parameters (q = 6, p = 12, respectively), originally related by a factor of two for simplicity of calculations. We propose the inference of the repulsion exponent through Hierarchical Bayesian uncertainty quantification We use experimental data of the radial distribution function and dimer interaction energies from quantum mechanics simulations. We find that the repulsion exponent p ≈ 6.5 provides an excellent fit for the experimental data of liquid argon, for a range of thermodynamic conditions, as well as for saturated argon vapour. Calibration using the quantum simulation data did not provide a good fit in these cases. However, values p ≈ 12.7 obtained by dimer quantum simulations are preferred for the argon gas while lower values are promoted by experimental data. These results show that the proposed LJ 6-p potential applies to a wider range of thermodynamic conditions, than the classical LJ 6-12 potential. We suggest that calibration of the repulsive exponent in the LJ potential widens the range of applicability and accuracy of MD simulations., Scientific Reports, 7, ISSN:2045-2322

We demonstrate that centered likelihood ratio estimators for the sensitivity indices of complex stochastic dynamics are highly efficient with low, constant in time variance and consequently they are suitable for sensitivity analysis in long-time and steady-state regimes. These estimators rely on a new covariance formulation of the likelihood ratio that includes as a submatrix a Fisher Information Matrix for stochastic dynamics and can also be used for fast screening of insensitive parameters and parameter combinations. The proposed methods are applicable to broad classes of stochastic dynamics such as chemical reaction networks, Langevin-type equations and stochastic models in finance, including systems with a high dimensional parameter space and/or disparate decorrelation times between different observables. Furthermore, they are simple to implement as a standard observable in any existing simulation algorithms without additional modifications., Comment: Revision of the paper. Added a new estimator

The instantaneous relative entropy (IRE) and the corresponding instantaneous Fisher information matrix (IFIM) for transient stochastic processes are presented in this paper. These novel tools for sensitivity analysis of stochastic models serve as an extension of the well known relative entropy rate (RER) and the corresponding Fisher information matrix (FIM) that apply to stationary processes. Three cases are studied here, discrete-time Markov chains, continuous-time Markov chains and stochastic differential equations. A biological reaction network is presented as a demonstration numerical example.

Existing sensitivity analysis approaches are not able to handle efficiently stochastic reaction networks with a large number of parameters and species, which are typical in the modeling and simulation of complex biochemical phenomena. In this paper, a two-step strategy for parametric sensitivity analysis for such systems is proposed, exploiting advantages and synergies between two recently proposed sensitivity analysis methodologies for stochastic dynamics. The first method performs sensitivity analysis of the stochastic dynamics by means of the Fisher Information Matrix on the underlying distribution of the trajectories; the second method is a reduced-variance, finite-difference, gradient-type sensitivity approach relying on stochastic coupling techniques for variance reduction. Here we demonstrate that these two methods can be combined and deployed together by means of a new sensitivity bound which incorporates the variance of the quantity of interest as well as the Fisher Information Matrix estimated from the first method. The first step of the proposed strategy labels sensitivities using the bound and screens out the insensitive parameters in a controlled manner. In the second step of the proposed strategy, a finite-difference method is applied only for the sensitivity estimation of the (potentially) sensitive parameters that have not been screened out in the first step. Results on an epidermal growth factor network with fifty parameters and on a protein homeostasis with eighty parameters demonstrate that the proposed strategy is able to quickly discover and discard the insensitive parameters and in the remaining potentially sensitive parameters it accurately estimates the sensitivities. The new sensitivity strategy can be several times faster than current state-of-the-art approaches that test all parameters, especially in "sloppy" systems. In particular, the computational acceleration is quantified by the ratio between the total number of parameters over the number of the sensitive parameters.

In this paper we propose a new class of coupling methods for the sensitivity analysis of high dimensional stochastic systems and in particular for lattice Kinetic Monte Carlo (KMC). Sensitivity analysis for stochastic systems is typically based on approximating continuous derivatives with respect to model parameters by the mean value of samples from a finite difference scheme. Instead of using independent samples the proposed algorithm reduces the variance of the estimator by developing a strongly correlated-"coupled"- stochastic process for both the perturbed and unperturbed stochastic processes, defined in a common state space. The novelty of our construction is that the new coupled process depends on the targeted observables, e.g., coverage, Hamiltonian, spatial correlations, surface roughness, etc., hence we refer to the proposed method as goal-oriented sensitivity analysis. In particular, the rates of the coupled Continuous Time Markov Chain are obtained as solutions to a goal-oriented optimization problem, depending on the observable of interest, by considering the minimization functional of the corresponding variance. We show that this functional can be used as a diagnostic tool for the design and evaluation of different classes of couplings. Furthermore, the resulting KMC sensitivity algorithm has an easy implementation that is based on the Bortz-Kalos-Lebowitz algorithm's philosophy, where events are divided in classes depending on level sets of the observable of interest. Finally, we demonstrate in several examples including adsorption, desorption, and diffusion Kinetic Monte Carlo that for the same confidence interval and observable, the proposed goal-oriented algorithm can be two orders of magnitude faster than existing coupling algorithms for spatial KMC such as the Common Random Number approach. We also provide a complete implementation of the proposed sensitivity analysis algorithms, including various spatial KMC examples, in a supplementary MATLAB source code.