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Modeling and simulation

About: Modeling and simulation is a research topic. Over the lifetime, 10273 publications have been published within this topic receiving 111550 citations.


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TL;DR: It is argued that many physical scenarios that have been problematic for modeling techniques manifest as nonconstructive models, and that constructive versions of some of the models properly reflect uncertainty in the behavior of the physical systems that plausibly arise from the principles of the underlying physics.
Abstract: This paper studies the semantics of models for discrete physical phenomena, such as rigid body collisions and switching in electronic circuits. This paper combines generalized functions (specifically the Dirac delta function), superdense time, modal models, and constructive semantics to get a rich, flexible, efficient, and rigorous approach to modeling such systems. It shows that many physical scenarios that have been problematic for modeling techniques manifest as nonconstructive models, and that constructive versions of some of the models properly reflect uncertainty in the behavior of the physical systems that plausibly arise from the principles of the underlying physics. This paper argues that these modeling difficulties are not reasonably solved by more detailed continuous models of the underlying physical phenomena. Such more detailed models simply shift the uncertainty to other aspects of the model. Since such detailed models come with a high computational cost, there is little justification in using them unless the goal of modeling is specifically to understand these more detailed physical processes. All models in this paper are implemented in the Ptolemy II modeling and simulation environment and made available online.

46 citations

Journal ArticleDOI
TL;DR: This work provides an overview of modeling approaches for RRAM simulation, at the level of technology computer aided design and high-level compact models for circuit simulations, including Finite element method modeling, kinetic Monte Carlo models, and physics-based analytical models.
Abstract: The semiconductor industry is currently challenged by the emergence of Internet of Things, Big data, and deep-learning techniques to enable object recognition and inference in portable computers. These revolutions demand new technologies for memory and computation going beyond the standard CMOS-based platform. In this scenario, resistive switching memory (RRAM) is extremely promising in the frame of storage technology, memory devices, and in-memory computing circuits, such as memristive logic or neuromorphic machines. To serve as enabling technology for these new fields, however, there is still a lack of industrial tools to predict the device behavior under certain operation schemes and to allow for optimization of the device properties based on materials and stack engineering. This work provides an overview of modeling approaches for RRAM simulation, at the level of technology computer aided design and high-level compact models for circuit simulations. Finite element method modeling, kinetic Monte Carlo models, and physics-based analytical models will be reviewed. The adaptation of modeling schemes to various RRAM concepts, such as filamentary switching and interface switching, will be discussed. Finally, application cases of compact modeling to simulate simple RRAM circuits for computing will be shown.

46 citations

Journal ArticleDOI
TL;DR: The models presented reproduce the main dynamic behavior of rigorous models and experimental data found in the literature, and the main tradeoffs arisen in process design are well described with these models, which is always an objective of optimal process design.

46 citations

Journal ArticleDOI
TL;DR: A hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches is presented, taking as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles.
Abstract: Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This “network-free” approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of “partial network expansion” into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings can be achieved using the new approach and a monetary cost analysis provides a practical measure of its utility.

46 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202333
202291
2021268
2020332
2019450
2018442