Measurement Based Approach

Measurement-based approaches focus on directly utilizing acquired data to understand and model complex systems, contrasting with simulation-based methods. Current research emphasizes integrating measurement data with simulations (e.g., using data assimilation and particle filters) to overcome limitations of incomplete or imperfect measurements, particularly in areas like human flow prediction and neural network hardware optimization. This approach is crucial for improving the accuracy and reliability of models across diverse fields, from urban planning to AI hardware development, by grounding models in real-world observations and accounting for inherent data imperfections.