Simulation Study
Simulation studies encompass the use of computational models to investigate complex systems and processes across diverse scientific domains. Current research emphasizes developing sophisticated models, including deep neural networks, agent-based models, and generative models, to enhance realism, efficiency, and the ability to handle large-scale datasets. These studies are crucial for testing hypotheses, optimizing designs, and predicting outcomes in scenarios ranging from weather forecasting and traffic flow to robotic control and drug discovery, ultimately advancing scientific understanding and informing practical applications. The increasing integration of large language models further expands the scope and accessibility of simulation studies.
Papers
Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World
Kiana Ehsani, Tanmay Gupta, Rose Hendrix, Jordi Salvador, Luca Weihs, Kuo-Hao Zeng, Kunal Pratap Singh, Yejin Kim, Winson Han, Alvaro Herrasti, Ranjay Krishna, Dustin Schwenk, Eli VanderBilt, Aniruddha Kembhavi
A Unified Simulation Framework for Visual and Behavioral Fidelity in Crowd Analysis
Niccolò Bisagno, Nicola Garau, Antonio Luigi Stefani, Nicola Conci
Algorithmic Persuasion Through Simulation
Keegan Harris, Nicole Immorlica, Brendan Lucier, Aleksandrs Slivkins
Bevel-Tip Needle Deflection Modeling, Simulation, and Validation in Multi-Layer Tissues
Yanzhou Wang, Lidia Al-Zogbi, Guanyun Liu, Jiawei Liu, Junichi Tokuda, Axel Krieger, Iulian Iordachita
mango: A Modular Python-Based Agent Simulation Framework
Rico Schrage, Jens Sager, Jan Philipp Hörding, Stefanie Holly
Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-making
Yueyuan Li, Songan Zhang, Mingyang Jiang, Xingyuan Chen, Yeqiang Qian, Chunxiang Wang, Ming Yang
Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving
Yueyuan Li, Wei Yuan, Songan Zhang, Weihao Yan, Qiyuan Shen, Chunxiang Wang, Ming Yang