Predicting new polynomials from known ones

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In summary, the conversation discusses the use of polynomials obtained through experiments to estimate cases where experiments have not been conducted. It is suggested that machine learning may be used, but more context is needed for a clearer answer.
  • #1
volican
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Not too sure which forum this would be best suited to. Say I have lots of polynomials that have been obtained through conducting experiments, with the different coefficients in the polynomial representing different physical properties that have been changed in each case. How could I use this data to estimate what the polynomial would be for cases where there has not been an experiment undertaken for those particular physical property values? Would machine learning be used to solve this?
 
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  • #2
Maybe. There is too much context missing for a clearer answer.
 
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  • #3
mfb said:
Maybe. There is too much context missing for a clearer answer.
I agree, e.g., are all the polynomials conducted about the same experiments and you want a single one that is the best? If so, what type of measure do you have to determine which is the best, etc.
 

Related to Predicting new polynomials from known ones

1. What is the purpose of predicting new polynomials from known ones?

The purpose of predicting new polynomials from known ones is to find patterns and relationships between existing polynomials in order to create new polynomials that follow the same pattern. This can be useful in a variety of fields such as mathematics, physics, and computer science.

2. How do you predict new polynomials from known ones?

To predict new polynomials from known ones, you can use various methods such as interpolation, extrapolation, and regression analysis. These methods involve using mathematical techniques to analyze the existing polynomials and make predictions about future values.

3. What are some real-life applications of predicting new polynomials from known ones?

Some real-life applications of predicting new polynomials from known ones include forecasting future stock prices, predicting population growth, and creating computer algorithms for image and sound recognition. It can also be used in predicting the behavior of chemical reactions and predicting the trajectory of objects in physics.

4. What are the limitations of predicting new polynomials from known ones?

One limitation of predicting new polynomials from known ones is that it relies on the assumption that the existing polynomials follow a specific pattern, which may not always be the case. Additionally, the accuracy of the predictions can be affected by factors such as outliers, measurement errors, and changes in the underlying data.

5. How can the accuracy of predictions be improved when predicting new polynomials from known ones?

To improve the accuracy of predictions when predicting new polynomials from known ones, it is important to use a large and diverse dataset, as well as to carefully consider the assumptions and limitations of the chosen prediction method. Additionally, regularly evaluating and adjusting the predictions based on new data can also help improve accuracy.

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