ThGaskin/NeuralABM
Neural parameter calibration for multi-agent models. Uses neural networks to estimate marginal densities on parameters and networks
This project helps researchers and scientists working with complex dynamic systems, especially those modeled by multi-agent systems, to better understand and calibrate their models. It takes observed data from a system and, using neural networks, estimates the underlying parameters and network structures that best explain that data. This allows users to accurately forecast, analyze, and gain insights from models like epidemic spreads or social interactions.
Use this if you need to determine the best parameters or infer hidden connections within your multi-agent system models from real-world data, especially for fields like epidemiology, social science, or engineering.
Not ideal if you are looking for a simple, off-the-shelf prediction model without the need for deep mechanistic understanding or parameter inference.
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32
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15
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Jupyter Notebook
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Last pushed
Feb 12, 2026
Commits (30d)
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