WIP on a Coordination Language to Automate the Generation of Co-Simulations

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Approximated stability analysis of bi-modal hybrid co-simulation scenarios

This is an accepted manuscript of an article published by Springer in: Cerone A., Roveri M. (eds) Software Engineering and Formal Methods. SEFM 2017. Lecture Notes in Computer Science, vol 10729, available online at: https://doi.org/10.1007/978-3-319-74781-1_24 The accepted version of the publication may differ from the final published version. For information on re-use, please refer to the publisher’s terms and conditions.Co-simulation is a technique to orchestrate multiple simulators in order to approximate the behavior of a coupled system as a whole. Simulators execute in a lockstep fashion, each exchanging inputs and output data points with the other simulators at pre-accorded times. In the context of systems with a physical and a cyber part, the communication frequency with which the simulators of each part communicate can have a negative impact in the accuracy of the global simulation results. In fact, the computed behavior can be qualitatively different, compared to the actual behavior of the original system, laying waste to potentially many hours of computation. It is therefore important to develop methods that answer whether a given communication frequency guarantees trustworthy co-simulation results. In this paper, we take a small step in that direction. We develop a technique to approximate the lowest frequency for which a particular set of simulation tools can exchange values in a co-simulation and obtain results that can be trusted.Published versio

CoSim20: An Integrated Development Environment for Accurate and Efficient Distributed Co-Simulations

International audienceThe development of Cyber-Physical Systems involves several disciplines and stakeholders, which use heterogeneous models and formalisms to specify the system and make early validation and verification. In order to understand the behaviour emerging from the heterogeneous models, a collaborative simulation (co-simulation) can be used. To make it happen, the system engineer must define a correct coordination of the different executable models, which can be distributed over different enterprises. This is an important but difficult (and error prone) task that can not be done without information about the behavioral semantics of each model. In this paper, we introduce an integrated development environment which allows 1) to import different executable models (named simulation units), 2) to graphically connect them with rich connectors and 3) to generate a dedicated, accurate and efficient distributed co-simulation. The framework is based on Eclipse EMF for the modeling part and on ∅MQ for the deployment. It is named CoSim20

Artificial intelligence and model checking methods for in silico clinical trials

Model-based approaches to safety and efficacy assessment of pharmacological treatments (In Silico Clinical Trials, ISCT) hold the promise to decrease time and cost for the needed experimentations, reduce the need for animal and human testing, and enable personalised medicine, where treatments tailored for each single patient can be designed before being actually administered. Research in Virtual Physiological Human (VPH) is harvesting such promise by developing quantitative mechanistic models of patient physiology and drugs. Depending on many parameters, such models define physiological differences among different individuals and different reactions to drug administrations. Value assignments to model parameters can be regarded as Virtual Patients (VPs). Thus, as in vivo clinical trials test relevant drugs against suitable candidate patients, ISCT simulate effect of relevant drugs against VPs covering possible behaviours that might occur in vivo. Having a population of VPs representative of the whole spectrum of human patient behaviours is a key enabler of ISCT. However, VPH models of practical relevance are typically too complex to be solved analytically or to be formally analysed. Thus, they are usually solved numerically within simulators. In this setting, Artificial Intelligence and Model Checking methods are typically devised. Indeed, a VP coupled together with a pharmacological treatment represents a closed-loop model where the VP plays the role of a physical subsystem and the treatment strategy plays the role of the control software. Systems with this structure are known as Cyber-Physical Systems (CPSs). Thus, simulation-based methodologies for CPSs can be employed within personalised medicine in order to compute representative VP populations and to conduct ISCT. In this thesis, we advance the state of the art of simulation-based Artificial Intelligence and Model Checking methods for ISCT in the following directions. First, we present a Statistical Model Checking (SMC) methodology based on hypothesis testing that, given a VPH model as input, computes a population of VPs which is representative (i.e., large enough to represent all relevant phenotypes, with a given degree of statistical confidence) and stratified (i.e., organised as a multi-layer hierarchy of homogeneous sub-groups). Stratification allows ISCT to adaptively focus on specific phenotypes, also supporting prioritisation of patient sub-groups in follow-up in vivo clinical trials. Second, resting on a representative VP population, we design an ISCT aiming at optimising a complex treatment for a patient digital twin, that is the virtual counterpart of that patient physiology defined by means of a set of VPs. Our ISCT employs an intelligent search driving a VPH model simulator to seek the lightest but still effective treatment for the input patient digital twin. Third, to enable interoperability among VPH models defined with different modelling and simulation environments and to increase efficiency of our ISCT, we also design an optimised simulator driver to speed-up backtracking-based search algorithms driving simulators. Finally, we evaluate the effectiveness of our presented methodologies on state-of-the-art use cases and validate our results on retrospective clinical data

Automated Validation of State-Based Client-Centric Isolation with TLA ⁺

Clear consistency guarantees on data are paramount for the design and implementation of distributed systems. When implementing distributed applications, developers require approaches to verify the data consistency guarantees of an implementation choice. Crooks et al. define a state-based and client-centric model of database isolation. This paper formalizes this state-based model in, reproduces their examples and shows how to model check runtime traces and algorithms with this formalization. The formalized model in enables semi-automatic model checking for different implementation alternatives for transactional operations and allows checking of conformance to isolation levels. We reproduce examples of the original paper and confirm the isolation guarantees of the combination of the well-known 2-phase locking and 2-phase commit algorithms. Using model checking this formalization can also help finding bugs in incorrect specifications. This improves feasibility of automated checking of isolation guarantees in synthesized synchronization implementations and it provides an environment for experimenting with new designs.</p

Step Revision in Hybrid Co-simulation with FMI

This paper presents a master algorithm for co-simulation of hybrid systems using the Functional Mock-up Interface (FMI) standard. Our algorithm introduces step revision to achieve an accurate and precise handling of mixtures of continuous-time and discrete-event signals, particularly in the situation where components are unable to accurately extrapolate their input. Step revision provides an efficient means to respect the error bounds of numerical approximation algorithms that operate inside co-simulated FMUs. We first explain the most fundamental issues associated with hybrid co-simulation and analyze them in the framework of FMI. We demonstrate the necessity for step revision to address some of these issues and formally describe a master algorithm that supports it. Finally, we present experimental results obtained through our reference implementation that is part of our publicly available open-source toolchain called FIDE.QC 20170110</p