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1. Empirical Validation of System Dynamics Cyber Security Models

   https://doi.org/10.1109/SoutheastCon42311.2019.9020607  

   Kannan, Uma; Swamidurai, Rajendran (May 2020, 2019 SoutheastCon)

   Model validation, though a process that's continuous and complex, establishes confidence in the soundness and usefulness of a model. Making sure that the model behaves similar to the modes of behavior seen in real systems, allows the builder of said model to assure accumulation of confidence in the model and thus validating the model. While doing this, the model builder is also required to build confidence from a target audience in the model through communicating to the bases. The basis of the system dynamics model validation, both in general and in the field of cyber security, relies on a casual loop diagram of the system being agreed upon by a group of experts. Model validation also uses formal quantitative and informal qualitative tools in addition to the validation techniques used by system dynamics. Amongst others, the usefulness of a model, in a user's eyes, is a valid standard by which we can evaluate them. To validate our system dynamics cyber security model, we used empirical structural and behavior tests. This paper describes tests of model structure and model behavior, which includes each test's purpose, the ways the tests were conducted, and empirical validation results using a proof-of-concept cyber security model. 

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2. On pattern formation in the thermodynamically-consistent variational Gray-Scott model

   https://doi.org/10.1016/j.mbs.2025.109453  

   Hao, Wenrui; Liu, Chun; Wang, Yiwei; Yang, Yahong (April 2025, Mathematical Biosciences)

   In this paper, we explore pattern formation in a four-species variational Gary-Scott model, which includes all reverse reactions and introduces a virtual species to describe the birth–death process in the classical Gray-Scott model. This modification transforms the classical Gray-Scott model into a thermodynamically consistent closed system. The classical two-species Gray-Scott model can be viewed as a subsystem of the variational model in the limiting case when the small parameter ε, related to the reaction rate of the reverse reactions, approaches zero. We numerically explore pattern formation in this physically more complete Gray-Scott model in one spatial dimension, using non-uniform steady states of the classical model as initial conditions. By decreasing ε, we observed that the stationary patterns in the classical Gray-Scott model can be stabilized as the transient states in the variational model for a significantly small ε. Additionally, the variational model admits oscillating and traveling wave-like patterns for small ε. The persistent time of these patterns is on the order of O(1/ε). We also analyze the energy stability of two uniform steady states in the variational Gary-Scott model for fixed. Although both states are stable in a certain sense, the gradient flow type dynamics of the variational model exhibit a selection effect based on the initial conditions, with pattern formation occurring only if the initial condition does not converge to the boundary steady state, which corresponds to the trivial uniform steady state in the classical Gray-Scott model. 

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3. Robustness guarantees for structured model reduction of dynamical systems with applications to biomolecular models

   https://doi.org/10.1002/rnc.6013  

   Pandey, Ayush; Murray, Richard M. (January 2022, International Journal of Robust and Nonlinear Control)

   Abstract Model reduction methods usually focus on the error performance analysis; however, in presence of uncertainties, it is important to analyze the robustness properties of the error in model reduction as well. This problem is particularly relevant for engineered biological systems that need to function in a largely unknown and uncertain environment. We give robustness guarantees for structured model reduction of linear and nonlinear dynamical systems under parametric uncertainties. We consider a model reduction problem where the states in the reduced model are a strict subset of the states of the full model, and the dynamics for all of the other states are collapsed to zero (similar to quasi‐steady‐state approximation). We show two approaches to compute a robustness guarantee metric for any such model reduction—a direct linear analysis method for linear dynamics and a sensitivity analysis based approach that also works for nonlinear dynamics. Using the robustness guarantees with an error metric and an input‐output mapping metric, we propose an automated model reduction method to determine the best possible reduced model for a given detailed system model. We apply our method for the (1) design space exploration of a gene expression system that leads to a new mathematical model that accounts for the limited resources in the system and (2) model reduction of a population control circuit in bacterial cells. 

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4. The Art and Science of Building a Computational Model to Understand Hemostasis

   https://doi.org/10.1055/s-0041-1722861  

   Leiderman, Karin; Sindi, Suzanne S.; Monroe, Dougald M.; Fogelson, Aaron L.; Neeves, Keith B. (February 2021, Seminars in Thrombosis and Hemostasis)

   null (Ed.)

   Abstract Computational models of various facets of hemostasis and thrombosis have increased substantially in the last decade. These models have the potential to make predictions that can uncover new mechanisms within the complex dynamics of thrombus formation. However, these predictions are only as good as the data and assumptions they are built upon, and therefore model building requires intimate coupling with experiments. The objective of this article is to guide the reader through how a computational model is built and how it can inform and be refined by experiments. This is accomplished by answering six questions facing the model builder: (1) Why make a model? (2) What kind of model should be built? (3) How is the model built? (4) Is the model a “good” model? (5) Do we believe the model? (6) Is the model useful? These questions are answered in the context of a model of thrombus formation that has been successfully applied to understanding the interplay between blood flow, platelet deposition, and coagulation and in identifying potential modifiers of thrombin generation in hemophilia A. 

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5. Power-aware Deep Learning Model Serving with µ-Serve. In Proceedings of the 2024 USENIX Annual Technical Conference (ATC 2024).

   Qiu, H; Mao, W; Patke, A; Cui, S; Jha, S; Wang, C; Franke, H; Kalbarczyk, Z; Basar, T; Iyer, R (September 2024, Usenix_Atc_24)

   Begnum, Kyrre; Border, Charles (Ed.)

   With the increasing popularity of large deep learning model serving workloads, there is a pressing need to reduce the energy consumption of a model-serving cluster while maintaining satisfied throughput or model-serving latency requirements. Model multiplexing approaches such as model parallelism, model placement, replication, and batching aim to optimize the model-serving performance. However, they fall short of leveraging the GPU frequency scaling opportunity for power saving. In this paper, we demonstrate (1) the benefits of GPU frequency scaling in power saving for model serving; and (2) the necessity for co-design and optimization of fine grained model multiplexing and GPU frequency scaling. We explore the co-design space and present a novel power-aware model-serving system, μ-Serve. μ-Serve is a model-serving framework that optimizes the power consumption and model serving latency/throughput of serving multiple ML models efficiently in a homogeneous GPU cluster. Evaluation results on production workloads show that μ-Serve achieves 1.2–2.6× power saving by dynamic GPU frequency scaling (up to 61% reduction) without SLO attainment violations. 

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