Scientific Simulation

Scientific simulation aims to create computational models that accurately represent real-world phenomena, enabling researchers to explore complex systems and test hypotheses efficiently. Current research emphasizes integrating machine learning techniques, such as neural networks, into simulation workflows to accelerate computationally expensive processes like reaching steady states and to improve the efficiency of Bayesian inference for model parameter estimation. This integration allows for more robust and efficient exploration of model parameter spaces, leading to improved predictive capabilities and a deeper understanding of complex systems across diverse scientific disciplines.