How to decide between 2 fields of research(Astrophysics vs Brain Modelling)?

Click For Summary

Discussion Overview

The discussion revolves around the decision-making process for choosing between two fields of research: Astrophysics/Planetary/Geophysics and ALife/Brain Modelling/Adaptive Learning. Participants explore the implications of each field, including their interests in computational and simulation science, and seek advice on how to navigate their indecision.

Discussion Character

  • Debate/contested
  • Exploratory
  • Technical explanation

Main Points Raised

  • One participant expresses a personal preference for neural networks, citing potential advances in that field.
  • Another participant questions whether the focus in Astrophysics would be on practical applications like weather and earthquake modeling or more theoretical astrophysics.
  • A participant mentions an interest in creating generic code for N-body simulations, indicating a desire to work across both fields depending on project direction.
  • There is a discussion about the importance of hardware considerations for simulations, particularly regarding parallel computing and code architecture.
  • One participant suggests that the choice of field may be influenced by recent readings, indicating how personal interests can shape decisions.
  • Another participant clarifies that while computability theory isn't directly applied in the two fields, it relates to understanding algorithms and numerical techniques relevant to 3D engine development.
  • There is a distinction made between computability theory and efficiency, with a participant arguing that computability is more about the feasibility of tasks rather than their efficiency.

Areas of Agreement / Disagreement

Participants express differing preferences for the fields of neural networks versus astrophysics, indicating a lack of consensus. The discussion includes multiple viewpoints on the relevance of computability theory and its application in the context of the proposed research areas.

Contextual Notes

Some participants note limitations in their understanding of how computability theory applies to the fields in question, highlighting a potential gap in knowledge regarding the intersection of these areas with computational techniques.

Who May Find This Useful

Individuals considering graduate studies in computational science, particularly in the fields of astrophysics or brain modeling, may find this discussion relevant. It may also benefit those interested in the implications of hardware choices on simulation projects.

neurocomp2003
Messages
1,359
Reaction score
4
Hi,
I get my grad school decision in about 2 weeks.:cry:
I'm having a problem deciding between 2 fields that I would love to do a research project in. Both involve Computational/Simulations Science [Computer Science(computability and 3D engine development) & Mathematics].

The 1st is ALife/Brain Modelling/Adaptive Learning(Neural Networks,Evolutionary Techniques).
The 2nd is Astrophysics/Planetary/Geophysics(multiscale science)

The question I have is how would you choose between the two? What questions would you ask if you were in my place?

I am passionate about both and began studying them at the undergrad level before I got lost. And i can see myself in either field. I have been told that you should only focus on one project which has led to this indecision.
Also is a student allowed to pursue 2 Phd's(1 after the other)? Is there any point?

Thank you for helping me, in advance
Jack
 
Physics news on Phys.org
I'd choose the neural network path, but that's just my personal preference. There are some great potential advances in the NN field, IMO. In your other choice, would you be focusing on practical things like Earth weather modelling and earthquake modelling, etc.? Or would you be wanting to focus more on the astrophysics aspect?
 
hi berkeman thanks for replying. I was thinking of designing code to handle both aspects actually. My main interest is creating generic code for N-body simulations of various levels of science but the project itself would be focused on any of those mentioned above(i guess it would depend on the consulting supervisor).

So you think there's greater potential in NNs and Adaptive Learning. I will have to take that into great consideration.
Thank you again.
 
What hardware are you targeting to run you code on? Will it rely on massive parallelism? For the kind of simulations that you are talking about, it would seem like a good idea early on to decide what physical platforms are best, and use that to help you plan your code architecture.
 
berkeman: I'm hoping to start off with one computer...for simplicity(still learning about Nbody)... but once I'm in the 2nd year(depending on how it is taught in grad school) i'd like to go into high computing/parallelism.
Do you know of any references(books/websites) i can take alook at to understand the difference in coding architecture that you mentioned above(going from a single PC to a supercomputer)
 
Last edited:
My choice would be neural networks, but that may be because I recently read Complexity, by Waldrop. :smile: I would second what berkeman had to say about this field, though.
 
I officially got accepted so does anyone else have advice? I got till sept to decide i think.
 
neurocomp2003 said:
Hi,
I get my grad school decision in about 2 weeks.:cry:
I'm having a problem deciding between 2 fields that I would love to do a research project in. Both involve Computational/Simulations Science [Computer Science(computability and 3D engine development) & Mathematics].

The 1st is ALife/Brain Modelling/Adaptive Learning(Neural Networks,Evolutionary Techniques).
The 2nd is Astrophysics/Planetary/Geophysics(multiscale science)
Neither of those involves computability theory. You mean algorithms and numerical techniques?

Personally I would also prefer neural networks and AI. The industrial age automated and made reproducible the work of manual labor--the computer age will automate and make reproducible the work of thought.
 
Last edited:
  • #10
computability theory isn't used directly in them but its about studying what's possible and what's not(or what's efficient and what's not), algorithms and numerical techniques IMO is part of the foundations of 3D-engine development.
 
Last edited:
  • #11
Computability theory is just about what's possible or what's not, not about efficiency. Pretty much anything you'd be doing in those fields is going to be possible. I think computability is applied more in fields like compiler design, where it helps to know that no matter what you do you can't write a perfect tool to detect unreachable code.
 
Last edited:

Similar threads

Other How do physicists practically switch fields to non-adjacent subfields?
  • · Replies 12 ·
Replies
12
Views
2K
Courses How should I decide on which research project to take on this year?
  • · Replies 1 ·
Replies
1
Views
1K
Other How much does one's undergraduate field of research matter?
  • · Replies 10 ·
Replies
10
Views
3K
Admissions Statement of purpose critique request (high-energy theory PhD application)
  • · Replies 7 ·
Replies
7
Views
4K
Other How to become an amateur physicist
  • · Replies 4 ·
Replies
4
Views
3K
What topics in a Physics degree don't appear in an Astrophysics degree?
  • · Replies 8 ·
Replies
8
Views
2K
Admissions How Can I Improve My PhD Research Statement?
  • · Replies 6 ·
Replies
6
Views
2K
Admissions Opening the door to an Astrophysics PhD after a 5 year gap
  • · Replies 11 ·
Replies
11
Views
3K
Deciding between Physics or Engineering after a very turbulent year
  • · Replies 23 ·
Replies
23
Views
3K
Admissions Curious about my grad school prospects
  • · Replies 3 ·
Replies
3
Views
4K