SimPADS: A Domain Specific Modeling Framework for Model-Based PADS DT&E

2017 ◽  
Author(s):  
James E. Moore ◽  
Oleg A. Yakimenko
Keyword(s):  
Modeling Framework ◽  
Domain Specific ◽  
Model Based ◽  
Domain Specific Modeling ◽  
Specific Modeling

scholarly journals Modeling for Rapid Systems Prototyping: Hospital Situational Awareness System Design

Systems ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 12
Author(s):  
Avi Shaked
Keyword(s):  
Situational Awareness ◽  
Functional System ◽  
Modeling Framework ◽  
Modeling Tools ◽  
Domain Specific ◽  
Model Based ◽  
Domain Specific Modeling ◽  
System Prototype ◽  
Specific Modeling ◽  
Awareness System

The COVID-19 pandemic caught hospitals unprepared. The need to treat patients remotely and with limited resources led hospitals to identify a gap in their operational situational awareness. During the pandemic, Israeli Aerospace Industries helped hospitals to address the gap by designing a system to support their effective operation, management and decision making. In this paper, we report on the development of a functional, working prototype of the system using model-based engineering approach and tools. Our approach relies on domain-specific modeling, incorporating metamodeling and domain-specific representations based on the problem domain’s ontology. The tools practiced are those embedded into the Eclipse Modeling Framework—specifically, Ecore Tools and Sirius. While these technological tools are typically used to create dedicated, engineering-related modeling tools, in this work, we use them to create a functional system prototype. We discuss the advantages of our approach as well as the challenges with respect to the existing tools and their underlying technology. Based on the reported experience, we encourage practitioners to adopt model-based engineering as an effective way to develop systems. Furthermore, we call researchers and tool developers to improve the state-of-the-art as well as the existing implementations of pertinent tools to support model-based rapid prototyping.

On Developing Hybrid Modeling Methods using Metamodeling Platforms

2015 ◽  
Vol 6 (1) ◽  
pp. 47-66
Author(s):  
Srdjan Zivkovic ◽  
Krzystof Miksa ◽  
Harald Kühn
Keyword(s):  
Complex Systems ◽  
Hybrid Modeling ◽  
Modeling Languages ◽  
Modeling Tools ◽  
Analysis And Design ◽  
Domain Specific ◽  
Modeling Methods ◽  
Model Based ◽  
Domain Specific Modeling ◽  
Specific Modeling

It has been acknowledged that model-based approaches and domain-specific modeling (DSM) languages, methods and tools are beneficial for the engineering of increasingly complex systems and software. Instead of general-purpose one-size-fits-all modeling languages, DSM methods facilitate model-based analysis and design of complex systems by providing modeling concepts tailored to the specific problem domain. Furthermore, hybrid DSM methods combine single DSM methods into integrated modeling methods, to allow for multi-perspective modeling. Metamodeling platforms provide flexible means for design and implementation of such hybrid modeling methods and appropriate domain-specific modeling tools. In this paper, we report on the conceptualization of a hybrid DSM method in the domain of network physical devices management, and its implementation based on the ADOxx metamodeling platform. The method introduces a hybrid modeling approach. A dedicated DSM language (DSML) is used to model the structure of physical devices and their configurations, whereas the formal language for knowledge representation OWL2 is used to specify configuration-related constraints. The outcome of the work is a hybrid, semantic technology-enabled DSM tool that allows for efficient and consistency-preserving model-based configuration of network equipment.

scholarly journals Accelerated Predictive Healthcare Analytics with Pumas, a High Performance Pharmaceutical Modeling and Simulation Platform

2020 ◽  
Author(s):  
Chris Rackauckas ◽  
Yingbo Ma ◽  
Andreas Noack ◽  
Vaibhav Dixit ◽  
Patrick Kofod Mogensen ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Automatic Differentiation ◽  
Quantitative Relationship ◽  
Estimation Methods ◽  
Stiff Systems ◽  
Modeling Framework ◽  
Domain Specific ◽  
Wide Range ◽  
Domain Specific Modeling ◽  
Specific Modeling

AbstractPharmacometric modeling establishes causal quantitative relationship between administered dose, tissue exposures, desired and undesired effects and patient’s risk factors. These models are employed to de-risk drug development and guide precision medicine decisions. Recent technological advances rendered collecting real-time and detailed data easy. However, the pharmacometric tools have not been designed to handle heterogeneous, big data and complex models. The estimation methods are outdated to solve modern healthcare challenges. We set out to design a platform that facilitates domain specific modeling and its integration with modern analytics to foster innovation and readiness to data deluge in healthcare.New specialized estimation methodologies have been developed that allow dramatic performance advances in areas that have not seen major improvements in decades. New ODE solver algorithms, such as coefficient-optimized higher order integrators and new automatic stiffness detecting algorithms which are robust to frequent discontinuities, give rise to up to 4x performance improvements across a wide range of stiff and non-stiff systems seen in pharmacometric applications. These methods combine with JIT compiler techniques and further specialize the solution process on the individual systems, allowing statically-sized optimizations and discrete sensitivity analysis via forward-mode automatic differentiation, to further enhance the accuracy and performance of the solving and parameter estimation process. We demonstrate that when all of these techniques are combined with a validated clinical trial dosing mechanism and non-compartmental analysis (NCA) suite, real applications like NLME parameter estimation see run times halved while retaining the same accuracy. Meanwhile in areas with less prior optimization of software, like optimal experimental design, we see orders of magnitude performance enhancements. Together we show a fast and modern domain specific modeling framework which lays a platform for innovation via upcoming integrations with modern analytics.

Sign in / Sign up

Export Citation Format

Share Document