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1. CSonNet: An Agent-Based Modeling Software System for Discrete Time Simulation

   https://doi.org/10.1109/WSC52266.2021.9715287  

   Priest, Joshua D.; Kishore, Aparna; Machi, Lucas; Kuhlman, Chris J.; Machi, Dustin; Ravi, S. S. (December 2021, 2021 Winter Simulation Conference (WSC))

   Contagion dynamics on networks are used to study many problems, including disease and virus epidemics, incarceration, obesity, protests and rebellions, needle sharing in drug use, and hurricane and other natural disaster events. Simulators to study these problems range from smaller-scale serial codes to large-scale distributed systems. In recent years, Python-based simulation systems have been built. In this work, we describe a new Python-based agent-based simulator called CSonNet. It differs from codes such as Epidemics on Networks in that it performs discrete time simulations based on the graph dynamical systems formalism. CSonNet is a parallel code; it implements concurrency through an embarrassingly parallel approach of running multiple simulation instances on a user-specified number of forked processes. It has a modeling framework whereby agent models are composed using a set of pre-defined state transition rules. We provide strong-scaling performance results and case studies to illustrate its features. 

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2. A Large-Scale Geographically Explicit Synthetic Population with Social Networks for the United States

   https://doi.org/10.1038/s41597-024-03970-1  

   Jiang, Na; Yin, Fuzhen; Wang, Boyu; Crooks, Andrew T (December 2024, Scientific Data)

   Within the geo-simulation research domain, micro-simulation and agent-based modeling often require the creation of synthetic populations. Creating such data is a time-consuming task and often lacks social networks, which are crucial for studying human interactions (e.g., disease spread, disaster response) while at the same time impacting decision-making. We address these challenges by introducing a Python based method that uses the open data including that from 2020 U.S. Census data to generate a large-scale realistic geographically explicit synthetic population for America’s 50 states and Washington D.C. along with the stylized social networks (e.g., home, work and schools). The resulting synthetic population can be utilized within various geo-simulation approaches (e.g., agent-based modeling), exploring the emergence of complex phenomena through human interactions and further fostering the study of urban digital twins. 

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3. Tissue Forge: Interactive biological and biophysics simulation environment

   https://doi.org/10.1371/journal.pcbi.1010768  

   Sego, T. J.; Sluka, James P.; Sauro, Herbert M.; Glazier, James A. (October 2023, PLOS Computational Biology)

   Kemp, Melissa L. (Ed.)

   Tissue Forge is an open-source interactive environment for particle-based physics, chemistry and biology modeling and simulation. Tissue Forge allows users to create, simulate and explore models and virtual experiments based on soft condensed matter physics at multiple scales, from the molecular to the multicellular, using a simple, consistent interface. While Tissue Forge is designed to simplify solving problems in complex subcellular, cellular and tissue biophysics, it supports applications ranging from classic molecular dynamics to agent-based multicellular systems with dynamic populations. Tissue Forge users can build and interact with models and simulations in real-time and change simulation details during execution, or execute simulations off-screen and/or remotely in high-performance computing environments. Tissue Forge provides a growing library of built-in model components along with support for user-specified models during the development and application of custom, agent-based models. Tissue Forge includes an extensive Python API for model and simulation specification via Python scripts, an IPython console and a Jupyter Notebook, as well as C and C++ APIs for integrated applications with other software tools. Tissue Forge supports installations on 64-bit Windows, Linux and MacOS systems and is available for local installation via conda. 

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4. Geographically-Explicit Synthetic Populations for Agent-based Models: A Gallery of Applications

   Jiang, N; Crooks, AT; Yin, F; Wang, B (November 2023, Proceedings of the 2023 Conference of The Computational Social Science Society of the Americas,)

   na (Ed.)

   Over the last two decades, there has been a growth in the applications of geographically-explicit agent-based models. One thing such models have in common is the creation of synthetic populations to initialize the artificial worlds in which the agents inhabit. One challenge such models face is that it is often difficult to create reusable geographically-explicit synthetic populations with social networks. In this paper, we introduce a Python based method that generates a reusable geographically-explicit synthetic population dataset along with its social networks. In addition, we present a pipeline for using the population datasets for model initialization. With this pipeline, multiple spatial and temporal scales of geographically-explicit agent-based models are presented focusing on Western New York. Such models not only demonstrate the utility of our synthetic population on commuting patterns but also how social networks can impact the simulation of disease spread and vaccination uptake. By doing so, this pipeline could benefit any modeler wishing to reuse synthetic populations with realistic geographic locations and social networks. 

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5. Evolving to Find Optimizations Humans Miss: Using Evolutionary Computation to Improve GPU Code for Bioinformatics Applications

   https://doi.org/10.1145/3703920  

   Liou, Jhe-Yu; Awan, Muaaz; Leyba, Kirtus; Šulc, Petr; Hofmeyr, Steven; Wu, Carole-Jean; Forrest, Stephanie (December 2024, ACM Transactions on Evolutionary Learning and Optimization)

   GPUs are used in many settings to accelerate large-scale scientific computation, including simulation, computational biology, and molecular dynamics. However, optimizing codes to run efficiently on GPUs requires developers to have both detailed understanding of the application logic and significant knowledge of parallel programming and GPU architectures. This paper shows that an automated GPU program optimization tool, GEVO, can leverage evolutionary computation to find code edits that reduce the runtime of three important applications, multiple sequence alignment, agent-based simulation and molecular dynamics codes, by 28.9%, 29%, and 17.8% respectively. The paper presents an in-depth analysis of the discovered optimizations, revealing that (1) several of the most important optimizations involve significant epistasis, (2) the primary sources of improvement are application-specific, and (3) many of the optimizations generalize across GPU architectures. In general, the discovered optimizations are not straightforward even for a GPU human expert, showcasing the potential of automated program optimization tools to both reduce the optimization burden for human domain experts and provide new insights for GPU experts. 

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