Update index.md

Author: fhyuancn

docs/index.md

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 # Integrating AI Tools with Physics-based Models
 
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 Spectral measurements acquired from spectrometers often contain unwanted signal components arising from background reflectance, absorption, scattering, and instrument-related artifacts. These interfering contributions can obscure the true spectral signature of the target material and reduce the accuracy of downstream analyses such as material identification, quantitative estimation, and machine learning-based prediction. On the other hand, using machine learning is not a suitable option either. We are dealing with physics in this problem, and traditional AI approaches are not robust enough. Their output is not reliable enough. Furthermore, we need a considerable amount of data to train the model to learn this physics, and there is still no guarantee that the model will be unbiased and reliable. So to answer this question we would like to explore that in what ways can AI tools and physics-based models can complement each other to mitigate their weaknesses.
 
 It can be accomplished in two ways. First, incorporating physics into AI models can lead to more realistic predictions and reduce the computational cost of physics-based models. Alternatively, AI tools can be employed to explore and address potential weaknesses in physics-based models. For instance, we can augment the input data of physics-based models to meet their data requirements. Additionally, if we encounter systematic bias in our data due to human or machine error during data collection, AI can help address these issues. Furthermore, simulations generated by physics-based models are often difficult to interpret and require rigorous data analysis to analyze, visualize, or extract useful information from them. AI tools can assist in interpreting these physics-based simulations and supporting the data analysis process.