Target: Build a ParaView/COMSOL-style scientific viewer in Rust using wgpu. Learner background: Computational mechanics researcher. Knows Rust, numerical methods, linear algebra. No graphics background.
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| # state file generated using paraview version 5.13.0 | |
| import glob | |
| import re | |
| import paraview | |
| paraview.compatibility.major = 5 | |
| paraview.compatibility.minor = 13 | |
| #### import the simple module from the paraview | |
| from paraview.simple import * |
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| #!/usr/bin/env bash | |
| set -euo pipefail | |
| # ── ParaView (Linux custom install) ────────────────────────── | |
| PARAVIEW_DIR="/home/dineshadepu/life/softwares/paraview" | |
| export LD_LIBRARY_PATH="${PARAVIEW_DIR}/lib${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}" | |
| PVPYTHON="${PARAVIEW_DIR}/bin/pvpython" | |
| # ── Paths ──────────────────────────────────────────────────── | |
| SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" |
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| use rayon::prelude::*; | |
| pub struct Particles { | |
| /// Total number of particles in the simulation. | |
| pub n: usize, | |
| pub x: Vec<f64>, | |
| pub u: Vec<f64>, | |
| pub f: Vec<f64>, | |
| /// Mass of particles. |
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| # User specific environment | |
| if ! [[ "$PATH" =~ "$HOME/.local/bin:$HOME/bin:" ]]; then | |
| PATH="$HOME/.local/bin:$HOME/bin:$PATH" | |
| fi | |
| export PATH | |
| # Uncomment the following line if you don't like systemctl's auto-paging feature: | |
| # export SYSTEMD_PAGER= |
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| /* | |
| ***************************************************** | |
| ***************************************************** | |
| ***************************************************** | |
| *** O O O O O O O O O O O O O O O O O O O O O O O *** | |
| *** O O O O O O O O O O O O O O O O O O O O O O O *** | |
| *** O O O O O O O O O O O O O O O O O O O O O O O *** | |
| *** O O O O O O O O O O O O O O O O O O O O O O O *** | |
| *** O O O O O O O O O O O O O O O O O O O O O O O *** |
To effectively learn Neural Networks, it’s best to build your understanding step-by-step, starting from foundational concepts in math and programming, and progressing through machine learning. Here's a clear learning path:
- Linear Algebra – Vectors, matrices, matrix multiplication
- Calculus – Derivatives, gradients (for backpropagation)
- Probability & Statistics – Basics of distributions, expectation, Bayes’ theorem
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