I have physical model prediction data as well as actual data. From this I can calculate the error of each prediction data point through simple subtraction. I am hoping to train a neural network to be able to assign an error to the input of the physical model.

My current plan is to normalize the error of each data point and assign it as a label to each model input. So the NN would be trained (and validated)on a 1000 data points with the associated error as a label. Once the model is trained I would be able to input one data point and the output of the neural network would be a single class, that is the error. The purpose this would serve would be to tune the physical prediction model.

I have position and velocity data for a satellite. This is going in as an input into an orbit propagation model. The output of this physics model is the position and velocity at some future point in time (2 weeks). This is subtracted from the actual position and velocity at the future point to obtain the error. The error is normalized and assigned to the original input to the orbit propagation model. My goal is to train a model to be able to assign error values (the output) to orbit parameters (the input) and predict what the error would be for any position and velocity vector. Would this kind of architecture work? If so, would you recommend a feedforward or RNN? Thank you.

Tags:

© 2019 Data Science Central ® Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Statistics -- New Foundations, Toolbox, and Machine Learning Recipes
- Book: Classification and Regression In a Weekend - With Python
- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions