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56 lines
2.1 KiB
Markdown
56 lines
2.1 KiB
Markdown
# COMPUTATIONAL TODO
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***MOVE EVERYTHING HERE OVER TO ISSUES IN THE GITHUB TRACKER***
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## Completed steps
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- implement 'launch function as a function' portion
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- substitute the transition functions into the optimality conditions.
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## Next steps
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- create the iterated optimality conditions
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- attach iterated state variables to iterated transitons
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- use these state variables to calculate the optimality condition values
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- use these optimality conditions to create a loss function
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- Thoughts on converting my `connect_transitions_to_otimality_conditions` work to this.
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I need to import torch into that section, and build a loss function.
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- The basics of this model
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- Use just a basic MSELoss wrapped so that it calculates
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- add boundary conditions to loss function
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- get a basic gradient descent/optimization of launch function working.
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- add satellite deorbit to model.
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- turn this into a framework in a module, not just a single notebook (long term goal)
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- turn testing_combined into an actual test setup
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- change prints to assertions
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- turn into functions
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- add into a testing framework
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- this isn't that important.
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## CONCERNS
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So I need to think about how to handle the launch functions.
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Currently, my launch function takes in the stocks and debris levels and returns a launch decision for each constellation.
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This is nice because it keeps them together, but it may require some thoughtful NeuralNetwork design later.
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The issue is that I need to set up a way to integrate multiple firms at the same time.
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This may be possible through how I set up the profit funcitons.
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Also, I think I need to write out some
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# Scratch work
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Writing out the functional forms that need to exist and the inheritance
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- Euler equation
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- Optimality Conditions
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- Transition functions
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- Loss function
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- Bounds
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- Euler equations
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- Neural net launch function
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Launch & Retire (a neural network)
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NN(states) -> launch & deorbit decisions
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Euler Equations
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EE(NN, states) -> vector of numbers
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Consists of
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Iterated_Optimality(Iterated_Value_Derivatives(NN), Iterated_States(NN))
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Loss Function
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L(EE, Bounds, NN, States) -> positive number |