Any thinking in applying evolutionary algorithms to fusion?

Click For Summary

Discussion Overview

The discussion revolves around the potential application of evolutionary algorithms, specifically cellular automata, to nuclear fusion research. Participants explore the feasibility of using these algorithms to optimize fusion processes and design, while considering the limitations of current models and the complexity of fusion systems.

Discussion Character

  • Exploratory
  • Debate/contested
  • Technical explanation

Main Points Raised

  • Some participants propose that evolutionary programs could enhance the efficiency of existing fusion methods and explore new approaches, despite the current limited application of such methods.
  • Others argue that the optimization of fusion reactors typically relies on established simulations and conventional minimization algorithms, questioning what specific aspects could be optimized through evolutionary algorithms.
  • A participant notes that successful application of genetic algorithms in fusion has occurred in specific cases, such as optimizing coil configurations, but emphasizes the need for accurate models to assess fitness in more complex scenarios.
  • It is suggested that evolutionary programming serves as an exploratory tool that can provide insights and guidance, rather than a definitive solution, with the potential to reveal previously unnoticed factors.
  • Concerns are raised about the vagueness of applying evolutionary algorithms to fusion without specific examples of problems that could benefit from this approach.
  • Some participants highlight the challenges of applying evolutionary algorithms to plasma geometries due to the current lack of understanding and accurate modeling in this area.
  • A participant from the medical field draws a parallel to advancements in protein folding and genomics, suggesting that similar progress in fusion research could eventually allow for better modeling and application of evolutionary algorithms.

Areas of Agreement / Disagreement

Participants express a mix of skepticism and curiosity regarding the application of evolutionary algorithms to fusion research. While some see potential in exploratory attempts, others emphasize the need for specificity and caution against vague assertions. No consensus is reached on the viability of these algorithms in the context of fusion.

Contextual Notes

Participants acknowledge limitations in current models of fusion processes, particularly regarding plasma geometries, which may hinder the effective application of evolutionary algorithms. The discussion reflects a range of perspectives on the potential and challenges of this approach.

Who May Find This Useful

This discussion may be of interest to researchers and practitioners in fields related to nuclear fusion, computational modeling, and evolutionary algorithms, as well as those exploring interdisciplinary applications of these concepts.

arupel
Messages
45
Reaction score
2
Has any thinking be done in applying evolutionary programs (cellular automata with a purpose) to fusion.

It would seem that the current methods that can be applied are few. Evolution, itself, has been described as the dumbest way of realizing the smartest methods of achieving its goals.

Evolutionary programs might be applied in making current methods of obtaining fusion more efficient and exploring other methods, now not known.

It has been used well in design optimization. I see no loss, whether it succeeds or not, at least in making such attempts, if they have not been already applied.

Any thinking on this?
 
Physics news on Phys.org
What exactly do you want to optimize that way?

You cannot build thousands of prototypes, you are limited to testing the most promising approaches. For a given type of fusion reactor, once you have a proper simulation of it, the parameters usually have optimal operation conditions that can be found by conventional minimization algorithms.
 
arupel said:
Has any thinking be done in applying evolutionary programs (cellular automata with a purpose) to fusion.

It would seem that the current methods that can be applied are few. Evolution, itself, has been described as the dumbest way of realizing the smartest methods of achieving its goals.

Evolutionary programs might be applied in making current methods of obtaining fusion more efficient and exploring other methods, now not known.

It has been used well in design optimization. I see no loss, whether it succeeds or not, at least in making such attempts, if they have not been already applied.

Any thinking on this?

In order to use a genetic algorithm you need a model that can be used to judge the fitness of an individual solution. In fusion research there are many situations where our models are not accurate enough.

I do know of a few cases where fusion scientist have used genetic algorithms in their research. For example, a friend of mine used a genetic algorithm to calculate an optimal set of coils. But this is a simple example where we know how to calculate the magnetic field from currents in a coil.
 
It is not necessary to build thousands of prototypes based upon the evolutionary programs. What it does is offer insights, possibilities, and possible guidance. It is not the end all of design processes. Standard scientific computer programs are then used to study these possibilities and see what information they bring up.

It is an exploratory tool that can reveal factors that would not otherwise be noticed.

An example is that evolutionary programming was used to determine a wing design. Results offered several possibilities, including one in which the wings swept forwards, not backwards. Standard computer simulations showed the validity of many of these designs.

Evolutionary programming needs criteria to evaluate which cells will propagate and which cells will die.
From what I understand we are not sure what these criteria should be in the fusion processes. Still it does not hurt to try in using best guesses and see what comes out. As an iterative process these criteria are can be changed.

I see no harm in trying evolutionary programming, but with initially low expectations.
What is possible is that the methodologies in using evolutionary programs are, in a sense, an evolutionary process. It gets better as you go along. Many efforts will be failures, but some may reveal roads which should be further explored.

What has been said about babies is that they have no practical use at the time they are babies.

There is no harming in trying it.

Just a random thought: use fuzzy logic with fuzzy criteria in evolutionary programming.
 
Last edited:
arupel said:
It is not necessary to build thousands of prototypes based upon the evolutionary programs. What it does offer are insights, possibilities, and possible guidance. It is not the end all of design processes. Standard scientific computer programs are then used to study these possibilities and see what information they bring up.

It is an exploratory tool that can reveal factors that would not otherwise be noticed.

An example is that evolutionary programming was used to determine a wing design. Results offered several possibilities, including one in which the wings swept forwards, not backwards. Standard computer simulations showed the validity of many of these designs.

Evolutionary programming needs criteria to evaluate which cells will propagate and which cells will die.
From what I understand we are not sure what these criteria should be in the fusion processes. Still it does not hurt to try in using best guesses and see what comes out.
As an iterative process these criteria are can be changed.

I see no harm in trying evolutionary programming, but with initially low expectations.
What is possible is that the methodologies in using evolutionary programs is, in a sense, an evolutionary process.
It gets better as you go along.

What has been said about babies is that they have no practical use at the time they are babies.

There is no harming in trying it.

Just a random thought: use fuzzy logic with fuzzy criteria in evolutionary programming.

The problem here is that you seem to be shooting in the dark, and expecting the rest of us to either justify your assertion, or falsify it.

You need to be VERY specific. Give a definite example of a "fusion" problem that is currently solved numerically that you think may be applicable to your "evolutionary programming". Simply saying that it might be applicable to "fusion" is extremely vague and superficial. This is not how it is done in a serious, scientific discussion.

So go pick out a specific problem, figure out how it was tackled, and then show clearly how this "evolutionary programming" is better.

Zz.
 
You can do this with wing geometries because we have good computer simulations of wings. Whatever the program comes up with can be tested to determine the fitness of this particular wing.

You can't do that with plasma geometries because we don't understand different geometries well enough to study them in an automated way in a computer.
arupel said:
There is no harming in trying it.
It takes time.
 
  • Like
Likes   Reactions: averow45
Arupel: thank you for an interesting idea. Not a physicist myself (just a fan) but I am surmising that the current models are still insufficient to apply evolutionary algorithms to (as others have alluded). I would guess that basic research in nuclear fusion is incredibly expensive and difficult to do at this point therefore the models remain incomplete. In my own field of Medicine we are starting to nail down the mysteries of protein folding, genomics, binding etc which is allowing some reasonable modelling to guide 'virtual' drug design. Good discussion by all thank you!

Tony Verow MD
 
Thanks for the limitations on plasma geometries. Perhaps at a later date evolutionary programs might be useful
 

Similar threads

  • · Replies 4 ·
Replies
4
Views
16K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 19 ·
Replies
19
Views
5K
  • · Replies 2 ·
Replies
2
Views
2K
  • Poll Poll
  • · Replies 12 ·
Replies
12
Views
3K
  • · Replies 5 ·
Replies
5
Views
3K
  • · Replies 5 ·
Replies
5
Views
2K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 7 ·
Replies
7
Views
2K
  • · Replies 6 ·
Replies
6
Views
2K