How is it to work in numerical relativity?

In summary, the problems/challenges that the author faces daily are related to code issues with the physics itself.
  • #1
MadAtom
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  1. the problems/challenges that you have to face daily are mostly related to code issues with the physics itself?
  2. Is there room to improve our knowledge of fundamental physics while working on it?
  3. Do you enjoy doing it? why?
I'm asking this because I'm considering working on numerical relativity but, although I really enjoy general relativity, I'm afraid that the problems that I will have to solve working in that field are mostly related to numerics and coding. Thank you in advance!

MA
 
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  • #2
I don't work in numerical relativity, but I have worked in numerical simulations in several other areas of physics. I can almost guarantee that the problems you will wrestle with on a daily basis will be almost entirely related to numerics and coding. Insights in fundamental physics will be few and far between. As Edison said, "Genius is one percent inspiration, ninety-nine percent perspiration". I think this is true in any field. Much of what you do on a daily basis is "grunt work". In my opinion, this doesn't make the work less enjoyable or less rewarding. On the contrary, when you achieve a new result, the fact that you have slogged through hundreds of trivial details to get there makes it all the more rewarding.
 
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  • #3
phyzguy said:
Much of what you do on a daily basis is "grunt work". In my opinion, this doesn't make the work less enjoyable or less rewarding. On the contrary, when you achieve a new result, the fact that you have slogged through hundreds of trivial details to get there makes it all the more rewarding.

I think this is really hard for students (or generally, young people) to understand.
 
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  • #4
phyzguy said:
I can almost guarantee that the problems you will wrestle with on a daily basis will be almost entirely related to numerics and coding.

I can relate 100%, most of my work is generally in finite element analysis and a lot of my day is just trying to get the damn models to mesh, lol.
 

1. What is numerical relativity?

Numerical relativity is a branch of physics that studies the behavior of objects in space and time using numerical methods. It combines concepts from Einstein's theory of general relativity with computational techniques to simulate and analyze complex physical phenomena such as black holes and gravitational waves.

2. What are the main challenges of working in numerical relativity?

One of the biggest challenges in numerical relativity is accurately representing the highly nonlinear nature of Einstein's equations. This requires advanced mathematical and computational techniques. Additionally, numerical simulations can be computationally expensive and time-consuming, making it challenging to study certain phenomena in a timely manner.

3. How does numerical relativity contribute to our understanding of the universe?

Numerical relativity allows us to study and simulate some of the most extreme and mysterious phenomena in the universe, such as black holes and gravitational waves. By analyzing the data from these simulations, we can gain a better understanding of the behavior and properties of these objects, as well as their effects on the surrounding space-time.

4. What are some practical applications of numerical relativity?

Numerical relativity has many practical applications, such as in the fields of astrophysics, cosmology, and gravitational wave detection. It can also be used to study and predict the behavior of objects in extreme environments, such as in the vicinity of black holes or in the early universe. Additionally, numerical relativity plays a crucial role in testing and validating theories of gravity.

5. What skills are required to work in numerical relativity?

To work in numerical relativity, one needs a strong background in physics, mathematics, and computer science. Some specific skills that are essential for this field include knowledge of general relativity, differential geometry, numerical analysis, and programming languages such as Fortran and C++. Strong problem-solving and critical thinking skills are also crucial for making progress in this challenging and complex field.

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