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Scientific Computing in Julia
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Module 1
Julia's Scientific Computing Ecosystem
1
Vectorized Numerical Computations with Array Comprehensions and Broadcasting
Write efficient, vectorized numerical computations using array comprehensions and broadcasting.
Matrix Decompositions with LinearAlgebra.jl
Perform standard matrix decompositions (LU, QR, SVD, Cholesky) using LinearAlgebra.jl.
3
Sparse Matrices in Julia for Physics Problems
Construct and manipulate sparse matrices for large-scale physics problems using SparseArrays.jl.
4
Publication-Quality Plots with Julia
Create publication-quality line plots, heatmaps, and multi-panel figures using a chosen plotting library (e.g., Plots.jl or Makie.jl).
5
Profiling Julia Code with @time and BenchmarkTools.jl
Profile Julia code to identify performance bottlenecks using @time and the BenchmarkTools.jl package.
6
Optimizing for Type Stability
Ensure type-stability in performance-critical code to maximize execution speed.
7
Saving and Loading Scientific Data
Save and load numerical data in standard scientific formats (e.g., using JLD2.jl or HDF5.jl).
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