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docs: publish via sweepx; drop the duplicated theme
The site is now the Solver section of sweepx, which owns the theme — so the second copy of it here (overrides/, extra.css, the hero landing page) is gone and Pages only redirects.
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‎.github/workflows/pages.yml‎

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name: Deploy docs to Pages
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name: Redirect docs to the sweepx site
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# This repo's docs are published as the Solver section of the sweepx umbrella
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# site, which owns the theme and builds every package's docs together. What
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# GitHub Pages serves from here is now only a redirect, so the old URLs — README
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# badges, paper links, bookmarks — keep working:
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#
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# /sweep/ → <TARGET>/
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# /sweep/api/equations/… → <TARGET>/api/equations/… (via 404.html)
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#
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# The path layout matches one-to-one, because the umbrella imports this docs/
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# flat. Deep links are mapped by 404.html: Pages serves it for any unknown path,
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# and its script re-attaches the tail to the target.
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#
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# TARGET is the umbrella's Solver section.
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on:
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push:
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group: pages
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cancel-in-progress: true
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env:
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TARGET: https://sweepx.deepwave.group/solver
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jobs:
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build:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v4
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- name: Setup Pages
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uses: actions/configure-pages@v5
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- name: Setup Python
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uses: actions/setup-python@v5
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with:
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python-version: '3.11'
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cache: 'pip'
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cache-dependency-path: docs/requirements.txt
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- name: Install dependencies
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- name: Generate the redirect site
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run: |
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pip install -r docs/requirements.txt
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# CPU-only torch wheel — sweep's optional torch backend is
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# imported at the top of several equation modules (das.py,
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# elasticP.py, visco_acoustic.py); without it, mkdocstrings
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# cannot resolve `sweep.equations.DAS` and the docs build
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# fails.
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pip install torch --index-url https://download.pytorch.org/whl/cpu
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# Install sweep itself (without the CUDA extension) so
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# mkdocstrings' force_inspection mode can import the
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# package — its docstring section comes from the runtime
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# `WaveEquation.__init_subclass__` admonition injection.
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pip install --no-build-isolation -e .
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mkdir -p _site
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cat > _site/index.html <<HTML
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<!doctype html>
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<html lang="en">
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<head>
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<meta charset="utf-8">
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<title>sweep-solver documentation moved</title>
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<link rel="canonical" href="${TARGET}/">
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<meta name="robots" content="noindex">
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<meta http-equiv="refresh" content="0; url=${TARGET}/">
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<script>location.replace("${TARGET}/" + location.hash);</script>
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</head>
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<body style="font:16px/1.6 system-ui,sans-serif;max-width:34rem;margin:15vh auto;padding:0 1rem">
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<h1 style="font-size:1.35rem">The docs moved</h1>
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<p>sweep-solver's documentation is now the <strong>Solver</strong> section of the
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sweepx site.</p>
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<p><a href="${TARGET}/">${TARGET}/</a></p>
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</body>
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</html>
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HTML
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- name: Build site
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run: mkdocs build
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# Pages serves 404.html for any path it doesn't have, which makes it a
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# catch-all: strip this project's base path, re-attach the rest.
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cat > _site/404.html <<HTML
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<!doctype html>
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<html lang="en">
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<head>
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<meta charset="utf-8">
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<title>Redirecting…</title>
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<meta name="robots" content="noindex">
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<script>
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(function () {
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var base = "/sweep/";
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var p = location.pathname;
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var rest = p.indexOf(base) === 0 ? p.slice(base.length) : "";
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location.replace("${TARGET}/" + rest + location.search + location.hash);
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})();
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</script>
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</head>
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<body style="font:16px/1.6 system-ui,sans-serif;max-width:34rem;margin:15vh auto;padding:0 1rem">
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<p>The docs moved to <a href="${TARGET}/">${TARGET}/</a>.</p>
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</body>
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</html>
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HTML
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- name: Upload artifact
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uses: actions/upload-pages-artifact@v3
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with:
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path: site
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path: _site
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deploy:
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environment:

‎README.md‎

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<h1 align="center">SWEEP</h1>
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<p align="center">
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<a href="https://deepwave-kaust.github.io/sweep/"><img alt="Docs" src="https://img.shields.io/badge/docs-online-blue?logo=readthedocs&logoColor=white"></a>
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<a href="https://sweepx.deepwave.group/solver/"><img alt="Docs" src="https://img.shields.io/badge/docs-online-blue?logo=readthedocs&logoColor=white"></a>
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<a href="https://opensource.org/licenses/MIT"><img alt="License: MIT" src="https://img.shields.io/badge/License-MIT-yellow.svg"></a>
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<a href="https://pytorch.org"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-2.0%2B-EE4C2C?logo=pytorch&logoColor=white"></a>
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</p>
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**Seismic Wave Equation Exploration Platform** — a differentiable framework for seismic wave-equation modeling, migration, and full-waveform inversion. One API, 20+ equations (acoustic / elastic / VTI / TTI / DAS), PyTorch and JAX backends, eager and compiled CUDA paths.
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📖 **Documentation**: <https://deepwave-kaust.github.io/sweep/>
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📖 **Documentation**: <https://sweepx.deepwave.group/solver/>
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## Install
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<sub>`sweepx` is the PyPI distribution name; you `import sweep` (the `scikit-learn` → `import sklearn`
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pattern, because the bare name `sweep` is taken on PyPI). `pip install sweep-solver` is equivalent.
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Full install notes are in [the docs](https://deepwave-kaust.github.io/sweep/getting-started/installation/).</sub>
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Full install notes are in [the docs](https://sweepx.deepwave.group/solver/getting-started/installation/).</sub>
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## Hello SWEEP
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‎README.zh-CN.md‎

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<h1 align="center">SWEEP</h1>
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<p align="center">
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<a href="https://deepwave-kaust.github.io/sweep/"><img alt="Docs" src="https://img.shields.io/badge/docs-online-blue?logo=readthedocs&logoColor=white"></a>
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<a href="https://sweepx.deepwave.group/solver/"><img alt="Docs" src="https://img.shields.io/badge/docs-online-blue?logo=readthedocs&logoColor=white"></a>
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<a href="https://opensource.org/licenses/MIT"><img alt="License: MIT" src="https://img.shields.io/badge/License-MIT-yellow.svg"></a>
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<a href="https://pytorch.org"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-2.0%2B-EE4C2C?logo=pytorch&logoColor=white"></a>
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</p>
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**Seismic Wave Equation Exploration Platform** —— 一个可微分的地震波方程正演、偏移与全波形反演框架。一套 API,20+ 种波动方程(声波 / 弹性 / VTI / TTI / DAS),支持 PyTorch 与 JAX 后端,eager 与编译 CUDA 两条实现路径。
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📖 **文档**: <https://deepwave-kaust.github.io/sweep/>
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📖 **文档**: <https://sweepx.deepwave.group/solver/>
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## 安装
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如果预编译无法自动检测 GPU 架构,在第二条命令前先设置 `TORCH_CUDA_ARCH_LIST`(如 V100 用 `"7.0"`、A100 用 `"8.0"`、RTX 6000 Ada 用 `"8.9"`)。
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<sub>`sweepx` 是 PyPI 发行名,导入用 `import sweep`(类似 `scikit-learn` → `import sklearn`,因为裸名 `sweep` 在 PyPI 已被占)。`pip install sweep-solver` 等价。完整说明见[文档](https://deepwave-kaust.github.io/sweep/getting-started/installation/)。</sub>
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<sub>`sweepx` 是 PyPI 发行名,导入用 `import sweep`(类似 `scikit-learn` → `import sklearn`,因为裸名 `sweep` 在 PyPI 已被占)。`pip install sweep-solver` 等价。完整说明见[文档](https://sweepx.deepwave.group/solver/getting-started/installation/)。</sub>
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## Hello SWEEP
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‎docs/figures/landing/feature_equations.png‎

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‎docs/index.md‎

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---
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template: home.html
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hide:
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- navigation
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- toc
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---
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<section class="sweep-features-3">
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<a class="sweep-features-3__card" href="user-guide/propagators/">
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<div class="sweep-features-3__tag">DIFFERENTIABLE</div>
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<h3 class="sweep-features-3__title">Gradients all the way down.</h3>
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<p class="sweep-features-3__desc">
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Every <code>PropTorch</code> call is an autograd node. <code>loss.backward()</code> populates <code>vp.grad</code> as an ordinary torch Tensor — feed it to any optimizer.
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</p>
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<span class="sweep-features-3__link">Learn more <span aria-hidden="true">→</span></span>
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</a>
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<a class="sweep-features-3__card" href="notebooks/12_multi_gpu/">
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<div class="sweep-features-3__tag">HPC · MULTI-GPU</div>
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<h3 class="sweep-features-3__title">Scale across GPUs.</h3>
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<p class="sweep-features-3__desc">
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Wrap the inversion in <code>torchrun --nproc_per_node=4</code> and shots distribute themselves. Marmousi FWI hits <strong>3.79×</strong> on 4 × V100.
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</p>
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<span class="sweep-features-3__link">Learn more <span aria-hidden="true">→</span></span>
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</a>
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<a class="sweep-features-3__card" href="api/">
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<div class="sweep-features-3__tag">PHYSICS ZOO</div>
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<h3 class="sweep-features-3__title">Acoustic to TTI, 2D and 3D.</h3>
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<p class="sweep-features-3__desc">
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33 equation classes across nine families — Acoustic, Elastic, VTI, TTI, LSRTM-Born, DAS. PML and sponge boundaries. Same solver API for all of them.
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</p>
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<span class="sweep-features-3__link">Learn more <span aria-hidden="true">→</span></span>
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</a>
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</section>
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<section class="sweep-onefile" markdown>
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<div class="sweep-onefile__inner" markdown>
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<div class="sweep-onefile__eyebrow">MINIFILM · HELLO · FWI</div>
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<h2 class="sweep-onefile__title">One file.<br><span class="sweep-onefile__dim">Forward → loss → backward.</span></h2>
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<p class="sweep-onefile__lede">A two-layer truth, one shot, sixty-four receivers — and a single <code>.backward()</code> hands you the velocity gradient.</p>
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```python
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import numpy as np, torch
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from sweep.equations import Acoustic
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from sweep.propagator.torch import PropTorch
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from sweep.signal import ricker
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dev = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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solver = PropTorch(Acoustic(device=dev),
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shape=(96, 128), dh=10.0, dt=2e-3, dev=dev)
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wavelet = ricker(np.arange(600) * 2e-3 - 0.12, f=10.0).astype(np.float32)
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sources = np.array([[64, 2]], dtype=np.int64)
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receivers = np.array([[[ix, 4] for ix in range(0, 128, 2)]], dtype=np.int64)
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vp = torch.tensor(vp_init, device=dev, requires_grad=True)
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pred = solver(wavelet, sources, receivers, models=[vp])
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loss = 0.5 * (pred - obs).pow(2).sum()
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loss.backward() # vp.grad ready for any torch.optim step
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```
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<a class="sweep-cta sweep-cta--ghost-dark" href="notebooks/00_hello_fwi/">Open the notebook →</a>
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</div>
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</section>
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<section class="sweep-features-2">
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<div class="sweep-feature-card">
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<div class="sweep-feature-card__tag">EQUATIONS</div>
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<h3 class="sweep-feature-card__title">Acoustic. Elastic.<br>VTI. TTI.</h3>
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<p class="sweep-feature-card__desc">
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Nine equation classes, one solver API. Swap <code>Acoustic</code> for <code>ElasticTTI</code> without touching your inversion loop.
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</p>
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<code class="sweep-feature-card__code">from sweep.equations import ElasticTTI</code>
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<div class="sweep-feature-card__fig">
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<img src="figures/landing/feature_equations.png" alt="Wavefield from elastic equation">
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</div>
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</div>
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<div class="sweep-feature-card">
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<div class="sweep-feature-card__tag">OPTIMIZER</div>
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<h3 class="sweep-feature-card__title">Adam, L-BFGS, or write<br>your own.</h3>
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<p class="sweep-feature-card__desc">
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Gradients are torch Tensors. <code>eps=1e-16</code> on Adam keeps tiny FWI gradients from getting masked.
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</p>
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<code class="sweep-feature-card__code">torch.optim.Adam([vp], lr=25.0, eps=1e-16)</code>
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<div class="sweep-feature-card__fig">
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<img src="figures/landing/feature_optimizer.png" alt="vp gradient image">
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</div>
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</div>
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</section>
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<section class="sweep-nb">
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<div class="sweep-nb__hd">
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<div class="sweep-nb__eyebrow">EXAMPLES</div>
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<h2 class="sweep-nb__title">Pre-baked notebooks.</h2>
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<p class="sweep-nb__lede">Every cell already executed. Read in the browser, or download to run locally.</p>
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</div>
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<div class="sweep-nb__grid">
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<a class="sweep-nb__featured" href="notebooks/01_fwi_acoustic_marmousi/">
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<div class="sweep-nb__featured-media">
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<img src="figures/gallery/01_fwi_acoustic_marmousi.png" alt="Marmousi FWI">
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</div>
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<div class="sweep-nb__featured-body">
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<span class="sweep-nb__chip teal">BENCHMARK · ACOUSTIC</span>
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<h4>Marmousi FWI</h4>
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<p>3.5 km × 17 km Marmousi at 25 m grid. 20 shots, 200 receivers, 4 s record. Adam (lr=25, eps=1e-16) on the compiled CUDA backend.</p>
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<div class="sweep-nb__stats">
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<div><span>RUNTIME</span><strong>6.8 s</strong></div>
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<div><span>ITERS</span><strong>30</strong></div>
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<div><span>LOSS</span><strong>17× drop</strong></div>
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</div>
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</div>
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</a>
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<div class="sweep-nb__side">
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<a class="sweep-nb__small" href="notebooks/05_wavefield_vti/">
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<span class="sweep-nb__chip amber">VTI · FORWARD</span>
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<h4>Diamond qP wavefront</h4>
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<p>Duveneck Fig. 2: ε = 0.25, δ = 0 on a 193 × 193 grid. Three VTI parameterizations side-by-side.</p>
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<div class="sweep-nb__small-media">
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<img src="figures/gallery/05_wavefield_vti.png" alt="VTI wavefield">
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</div>
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</a>
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<a class="sweep-nb__cta" href="examples/">
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<div class="sweep-nb__cta-num">21 notebooks · 9 equations</div>
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<div class="sweep-nb__cta-title">See every<br><span class="sweep-nb__cta-italic">example.</span></div>
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<div class="sweep-nb__cta-tags">
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<span>FWI</span><span>RTM</span><span>LSRTM</span><span>Elastic</span><span>VTI</span><span>TTI</span><span>DAS</span><span>3D</span><span>Multi-GPU</span>
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</div>
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<div class="sweep-nb__cta-arrow">Browse all examples →</div>
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</a>
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</div>
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</div>
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</section>
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<section class="sweep-stack">
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<div class="sweep-stack__eyebrow">THE STACK</div>
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<h2 class="sweep-stack__title">Built on what you already trust.</h2>
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<p class="sweep-stack__lede">Lazy imports — only the backend you actually use is loaded.</p>
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<div class="sweep-stack__grid">
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<div class="sweep-stack__card">
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<div class="sweep-stack__head"><span class="sweep-stack__dot" style="background:#1AA690"></span><span class="sweep-stack__role">PRIMARY</span></div>
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<div class="sweep-stack__name">PyTorch</div>
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<div class="sweep-stack__version">≥ 2.1</div>
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<div class="sweep-stack__desc">Default training backend. CUDA + CPU + MPS.</div>
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</div>
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<div class="sweep-stack__card">
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<div class="sweep-stack__head"><span class="sweep-stack__dot sweep-stack__dot--tri" style="border-bottom-color:#BCC83C"></span><span class="sweep-stack__role">PRIMARY</span></div>
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<div class="sweep-stack__name">JAX</div>
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<div class="sweep-stack__version">≥ 0.4.20</div>
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<div class="sweep-stack__desc">Functional autograd, jit, vmap.</div>
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</div>
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<div class="sweep-stack__card">
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<div class="sweep-stack__head"><span class="sweep-stack__dot sweep-stack__dot--sq" style="background:#ED8B2E"></span><span class="sweep-stack__role">GPU</span></div>
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<div class="sweep-stack__name">CUDA</div>
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<div class="sweep-stack__version">11 / 12</div>
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<div class="sweep-stack__desc">NVIDIA V100, A100, L40S, RTX 6000 Ada.</div>
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</div>
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<div class="sweep-stack__card">
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<div class="sweep-stack__head"><span class="sweep-stack__dot" style="background:#1AA690"></span><span class="sweep-stack__role">ALWAYS-ON</span></div>
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<div class="sweep-stack__name">NumPy</div>
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<div class="sweep-stack__version">≥ 1.26</div>
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<div class="sweep-stack__desc">Numerical interop, IO.</div>
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</div>
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# sweep-solver
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The differentiable, GPU **wave-equation engine** at the core of the sweep
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ecosystem — forward modelling, RTM, and FWI / LSRTM, with equations for
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acoustic, elastic, VTI/TTI, VRZ and spectral-element (SEM) physics, and
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torch / JAX / native-CUDA backends.
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Installed with `pip install sweepx` (or `pip install sweep-solver`) → `import sweep`.
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<div class="grid cards" markdown>
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- :material-rocket-launch-outline: __[Getting started](getting-started/installation.md)__
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---
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Install, run your first forward model, take your first gradient.
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- :material-book-open-variant-outline: __[User guide](user-guide/equations.md)__
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---
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Equations, propagators, backends, boundary-saving, memory options.
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- :material-notebook-outline: __[Examples](examples/index.md)__
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---
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Runnable notebooks — modelling, wavefields, FWI, RTM.
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- :material-api: __[API reference](api/index.md)__
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---
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Every equation, propagator, and operator, documented from source.
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</div>
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</section>

‎docs/javascripts/katex.js‎

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