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
Simulating The U.S. Senate: An LLM-Driven Agent Approach to Modeling Legislative Behavior and Bipartisanship
Zachary R. Baker, Zarif L. Azher
VDG: Vision-Only Dynamic Gaussian for Driving Simulation
Hao Li, Jingfeng Li, Dingwen Zhang, Chenming Wu, Jieqi Shi, Chen Zhao, Haocheng Feng, Errui Ding, Jingdong Wang, Junwei Han
Simulating, Fast and Slow: Learning Policies for Black-Box Optimization
Fabio Valerio Massoli, Tim Bakker, Thomas Hehn, Tribhuvanesh Orekondy, Arash Behboodi
Spherinator and HiPSter: Representation Learning for Unbiased Knowledge Discovery from Simulations
Kai L. Polsterer, Bernd Doser, Andreas Fehlner, Sebastian Trujillo-Gomez