Body: <p>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.</p>
  
  <p>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. Would this kind of architecture work? If so, would you recommend a feedforward or RNN? Thank you.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-15T07:18:59.263Z
Comments.Text: Correct, I am not attempting to find errors in the NN. The errors are coming from the physical model's predicted output and the "real" values. These errors are assigned to the corresponding physical model's input as classes and this serves as the input to the neural network.
Comments.UserDisplayName: ashune
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-15T12:56:28.610Z
Comments.Text: pls explain in detail what kind of data do you have on input or output.
Comments.UserDisplayName: user8426627
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-15T15:04:13.013Z
Comments.Text: 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.
Comments.UserDisplayName: ashune
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-15T21:40:53.740Z
Comments.Text: Autoencoders are unsupervised. I'm not sure how I could use that in my case.
Comments.UserDisplayName: ashune
Tags: <neural-networks>
Title: Neural Network for Error Prediction of a Physics Model?
UserDisplayName: ashune
ThreadText: # Neural Network for Error Prediction of a Physics Model?
  
  (Asked by: username_0 on 2019-06-15)
  
  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. Would this kind of architecture work? If so, would you recommend a feedforward or RNN? Thank you.
  
  **Tags:** neural-networks
  > **Comments:**
  > **username_0**: Correct, I am not attempting to find errors in the NN. The errors are coming from the physical model's predicted output and the "real" values. These errors are assigned to the corresponding physical model's input as classes and this serves as the input to the neural network.  
  > **username_1**: pls explain in detail what kind of data do you have on input or output.  
  > **username_0**: 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.  
  > **username_0**: Autoencoders are unsupervised. I'm not sure how I could use that in my case.  
  
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  ## 0 answers
  
  