r/ScientificComputing

why is my Gaussian Elimination algorithm doesn't work, especially the second part, I made a lot more details on why I think it doesn't here https://scicomp.stackexchange.com/questions/45561/gaussian-elimination-2nd-part but I can't get it completely. can anyone help?

import numpy as np

A=np.array([[4,8,12],[3,8,13],[2,9,18]])
b=np.array([4,5,11])

def Gauss_Elimination(A,b):
    n = np.shape(A)[0]

    # 1st Part
    for k in range(n):
        pivot_element=A[k,k]
        # if pivot_element==0: ### I'll deal with this condition later
        for j in range(k+1,n):
            b[j]=b[j]-(A[j,k]/pivot_element)*b[k]
            A[j,:]=A[j,:]-(A[j,k]/pivot_element)*A[k,:]

        # 2nd Part
        x = np.zeros(n)
        x[-1]=b[-1]/A[-1,-1]
        for i in range(n-2,-1,-1):
        x[i]=(b[i]-np.sum(x[i+1]-A[i,i+1]))/A[i,i]
    return A,b, x # not necessary since it's call by reference

Gauss_Elimination(A,b)
print(A,'\n',b)

I am pursuing a dual degree in math and physics for undergrad and focusing on mathematical physics in grad school (maybe string theory but most certainly particle physics). I am going to begin learning coding languages in advance so that I can do computational physics for necessary simulations. I was wondering what the most important ones would be for this specific focus?

Hi!

I'm a Computer Science graduate and want to build a career in Scientific Computing/Computational Science. I'm considering pursuing masters in Computational Science because I really like this field, but I have some concerns.

I wanted to ask

• If it is reasonable for someone with a Computer Science background to study Computational Science, given that I would be competing with people from Mathematics and Physics backgrounds? I feel that Computational Science is much more closer to Mathematics and Physics than to Computer Science.
• How AI/LLM might affect this field, since it can make programming easier than it was before.
• Are there good career opportunities in this field in the current job market?

Thanks!

i'm an undergrad physics student trying to learn the basics of scienctific computing like numpy, matplotlib and scipy (preferably in that order)- what courses can I refer to (preferably free, but I have no aversion to paid courses either). Is the 'Scipy 2019' playlist by Enthought on youtube good? It seems to be fairly detailed, open to suggestions :)

I’m not a beginner programmer, but I’m relatively new to scientific programming. I’ve recently started doing scientific research with a professor, and a significant part of my work involves writing simulations, modeling physical systems and mathematical equations computationally, and developing algorithms to analyze or reproduce these systems.

I’m interested in becoming much stronger at this side of programming, particularly in areas like numerical methods, computational physics, mathematical modeling, simulation, and scientific computing.

For those of you with experience in scientific programming, what would you recommend as the best way to build these skills? I’d especially appreciate recommendations for textbooks, courses, projects, libraries, or other resources that helped you make the transition from general programming to scientific/computational programming.

I’m comfortable with programming fundamentals, so I’m primarily looking for resources that go beyond the basics and focus on the actual mathematics, algorithms, and computational techniques behind scientific programming.

Any advice or recommendations would be greatly appreciated!