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
Value Alignment and Trust in Human-Robot Interaction: Insights from Simulation and User Study
Shreyas Bhat, Joseph B. Lyons, Cong Shi, X. Jessie Yang
LLM experiments with simulation: Large Language Model Multi-Agent System for Simulation Model Parametrization in Digital Twins
Yuchen Xia, Daniel Dittler, Nasser Jazdi, Haonan Chen, Michael Weyrich
Network Diffusion -- Framework to Simulate Spreading Processes in Complex Networks
Michał Czuba, Mateusz Nurek, Damian Serwata, Yu-Xuan Qiu, Mingshan Jia, Katarzyna Musial, Radosław Michalski, Piotr Bródka
Sensitivity Analysis for Active Sampling, with Applications to the Simulation of Analog Circuits
Reda Chhaibi, Fabrice Gamboa, Christophe Oger, Vinicius Oliveira, Clément Pellegrini, Damien Remot
Integrating Multi-Physics Simulations and Machine Learning to Define the Spatter Mechanism and Process Window in Laser Powder Bed Fusion
Olabode T. Ajenifujah, Francis Ogoke, Florian Wirth, Jack Beuth, Amir Barati Farimani
Intrinsic Rewards for Exploration without Harm from Observational Noise: A Simulation Study Based on the Free Energy Principle
Theodore Jerome Tinker, Kenji Doya, Jun Tani