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
Using simulation to design an MPC policy for field navigation using GPS sensing
Harry Zhang, Stefan Caldararu, Ishaan Mahajan, Shouvik Chatterjee, Thomas Hansen, Abhiraj Dashora, Sriram Ashokkumar, Luning Fang, Xiangru Xu, Shen He, Dan Negrut
Multi-robot Motion Planning based on Nets-within-Nets Modeling and Simulation
Sofia Hustiu, Eva Robillard, Joaquin Ezpeleta, Cristian Mahulea, Marius Kloetzer
On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods
Artur P. Toshev, Ludger Paehler, Andrea Panizza, Nikolaus A. Adams
Exploring Global Climate Cooperation through AI: An Assessment of the AI4GCC Framework by simulations
Xavier Marjou, Arnaud Braud, Gaël Fromentoux
PINNSim: A Simulator for Power System Dynamics based on Physics-Informed Neural Networks
Jochen Stiasny, Baosen Zhang, Spyros Chatzivasileiadis
Posterior Estimation Using Deep Learning: A Simulation Study of Compartmental Modeling in Dynamic PET
Xiaofeng Liu, Thibault Marin, Tiss Amal, Jonghye Woo, Georges El Fakhri, Jinsong Ouyang