Model Parameter

Model parameters are the adjustable values within mathematical models that determine their behavior and predictions. Current research focuses on efficiently estimating these parameters from data, often employing neural networks—including deep learning architectures like MLPs and autoencoders—to solve inverse problems and improve model accuracy. This work is crucial for enhancing the reliability and efficiency of various applications, ranging from improving AI system robustness to optimizing complex physical simulations in fields like fire science and material modeling. Furthermore, research is addressing challenges like parameter identifiability and the trade-offs between model complexity and accuracy.