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Documenter.jl committed Apr 8, 2024
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2 changes: 1 addition & 1 deletion dev/.documenter-siteinfo.json
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{"documenter":{"julia_version":"1.10.2","generation_timestamp":"2024-04-05T01:24:29","documenter_version":"1.3.0"}}
{"documenter":{"julia_version":"1.10.2","generation_timestamp":"2024-04-08T13:14:33","documenter_version":"1.3.0"}}
6 changes: 3 additions & 3 deletions dev/DeepBSDE/index.html
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trajectories=m,
sdealg=StochasticDiffEq.,
dt=dt,
pabstol = 1f-6)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/HighDimPDE.jl/blob/cf21830dec39c01a7e5b1d2b51f16a1ed3c1ab70/src/DeepBSDE.jl#L1-L52">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="CommonSolve.solve-Tuple{ParabolicPDEProblem, DeepBSDE, Any}" href="#CommonSolve.solve-Tuple{ParabolicPDEProblem, DeepBSDE, Any}"><code>CommonSolve.solve</code></a> — <span class="docstring-category">Method</span></header><section><div><pre><code class="language-julia hljs">solve(
pabstol = 1f-6)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/HighDimPDE.jl/blob/4b78e9d0df37cc150edfddc515235f34d26ffed0/src/DeepBSDE.jl#L1-L52">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="CommonSolve.solve-Tuple{ParabolicPDEProblem, DeepBSDE, Any}" href="#CommonSolve.solve-Tuple{ParabolicPDEProblem, DeepBSDE, Any}"><code>CommonSolve.solve</code></a> — <span class="docstring-category">Method</span></header><section><div><pre><code class="language-julia hljs">solve(
prob::ParabolicPDEProblem,
pdealg::DeepBSDE,
sdealg;
Expand All @@ -49,7 +49,7 @@
maxiters_limits,
kwargs...
) -&gt; PIDESolution
</code></pre><p>Returns a <code>PIDESolution</code> object.</p><p><strong>Arguments</strong></p><ul><li><code>sdealg</code>: a SDE solver from <a href="https://diffeq.sciml.ai/stable/solvers/sde_solve/">DifferentialEquations.jl</a>. If not provided, the plain vanilla <a href="https://arxiv.org/abs/1707.02568">DeepBSDE</a> method will be applied. If provided, the SDE associated with the PDE problem will be solved relying on methods from DifferentialEquations.jl, using <a href="https://diffeq.sciml.ai/stable/features/ensemble/">Ensemble solves</a> via <code>sdealg</code>. Check the available <code>sdealg</code> on the <a href="https://diffeq.sciml.ai/stable/solvers/sde_solve/">DifferentialEquations.jl doc</a>.</li><li><code>limits</code>: if <code>true</code>, upper and lower limits will be calculated, based on <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3071506">Deep Primal-Dual algorithm for BSDEs</a>.</li><li><code>maxiters</code>: The number of training epochs. Defaults to <code>300</code></li><li><code>trajectories</code>: The number of trajectories simulated for training. Defaults to <code>100</code></li><li>Extra keyword arguments passed to <code>solve</code> will be further passed to the SDE solver.</li></ul></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/HighDimPDE.jl/blob/cf21830dec39c01a7e5b1d2b51f16a1ed3c1ab70/src/DeepBSDE.jl#L61">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="CommonSolve.solve-Tuple{ParabolicPDEProblem, DeepBSDE}" href="#CommonSolve.solve-Tuple{ParabolicPDEProblem, DeepBSDE}"><code>CommonSolve.solve</code></a> — <span class="docstring-category">Method</span></header><section><div><pre><code class="language-julia hljs">solve(
</code></pre><p>Returns a <code>PIDESolution</code> object.</p><p><strong>Arguments</strong></p><ul><li><code>sdealg</code>: a SDE solver from <a href="https://diffeq.sciml.ai/stable/solvers/sde_solve/">DifferentialEquations.jl</a>. If not provided, the plain vanilla <a href="https://arxiv.org/abs/1707.02568">DeepBSDE</a> method will be applied. If provided, the SDE associated with the PDE problem will be solved relying on methods from DifferentialEquations.jl, using <a href="https://diffeq.sciml.ai/stable/features/ensemble/">Ensemble solves</a> via <code>sdealg</code>. Check the available <code>sdealg</code> on the <a href="https://diffeq.sciml.ai/stable/solvers/sde_solve/">DifferentialEquations.jl doc</a>.</li><li><code>limits</code>: if <code>true</code>, upper and lower limits will be calculated, based on <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3071506">Deep Primal-Dual algorithm for BSDEs</a>.</li><li><code>maxiters</code>: The number of training epochs. Defaults to <code>300</code></li><li><code>trajectories</code>: The number of trajectories simulated for training. Defaults to <code>100</code></li><li>Extra keyword arguments passed to <code>solve</code> will be further passed to the SDE solver.</li></ul></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/HighDimPDE.jl/blob/4b78e9d0df37cc150edfddc515235f34d26ffed0/src/DeepBSDE.jl#L61">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="CommonSolve.solve-Tuple{ParabolicPDEProblem, DeepBSDE}" href="#CommonSolve.solve-Tuple{ParabolicPDEProblem, DeepBSDE}"><code>CommonSolve.solve</code></a> — <span class="docstring-category">Method</span></header><section><div><pre><code class="language-julia hljs">solve(
prob::ParabolicPDEProblem,
alg::DeepBSDE;
dt,
Expand All @@ -64,4 +64,4 @@
trajectories_lower,
maxiters_limits
)
</code></pre><p>Returns a <code>PIDESolution</code> object. </p><p><strong>Arguments:</strong></p><ul><li><code>maxiters</code>: The number of training epochs. Defaults to <code>300</code></li><li><code>trajectories</code>: The number of trajectories simulated for training. Defaults to <code>100</code></li></ul><p>To use <a href="https://diffeq.sciml.ai/stable/solvers/sde_solve/">SDE Algorithms</a> use <a href="../tutorials/deepbsde/#DeepBSDE"><code>DeepBSDE</code></a></p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/HighDimPDE.jl/blob/cf21830dec39c01a7e5b1d2b51f16a1ed3c1ab70/src/DeepBSDE_Han.jl#L2">source</a></section></article><h2 id="The-general-idea"><a class="docs-heading-anchor" href="#The-general-idea">The general idea 💡</a><a id="The-general-idea-1"></a><a class="docs-heading-anchor-permalink" href="#The-general-idea" title="Permalink"></a></h2><p>The <code>DeepBSDE</code> algorithm is similar in essence to the <code>DeepSplitting</code> algorithm, with the difference that it uses two neural networks to approximate both the the solution and its gradient.</p><h2 id="References"><a class="docs-heading-anchor" href="#References">References</a><a id="References-1"></a><a class="docs-heading-anchor-permalink" href="#References" title="Permalink"></a></h2><ul><li>Han, J., Jentzen, A., E, W., Solving high-dimensional partial differential equations using deep learning. <a href="https://arxiv.org/abs/1707.02568">arXiv</a> (2018)</li></ul></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../DeepSplitting/">« The <code>DeepSplitting</code> algorithm</a><a class="docs-footer-nextpage" href="../NNStopping/">The <code>NNStopping</code> algorithm »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.3.0 on <span class="colophon-date" title="Friday 5 April 2024 01:24">Friday 5 April 2024</span>. Using Julia version 1.10.2.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
</code></pre><p>Returns a <code>PIDESolution</code> object. </p><p><strong>Arguments:</strong></p><ul><li><code>maxiters</code>: The number of training epochs. Defaults to <code>300</code></li><li><code>trajectories</code>: The number of trajectories simulated for training. Defaults to <code>100</code></li></ul><p>To use <a href="https://diffeq.sciml.ai/stable/solvers/sde_solve/">SDE Algorithms</a> use <a href="../tutorials/deepbsde/#DeepBSDE"><code>DeepBSDE</code></a></p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/HighDimPDE.jl/blob/4b78e9d0df37cc150edfddc515235f34d26ffed0/src/DeepBSDE_Han.jl#L2">source</a></section></article><h2 id="The-general-idea"><a class="docs-heading-anchor" href="#The-general-idea">The general idea 💡</a><a id="The-general-idea-1"></a><a class="docs-heading-anchor-permalink" href="#The-general-idea" title="Permalink"></a></h2><p>The <code>DeepBSDE</code> algorithm is similar in essence to the <code>DeepSplitting</code> algorithm, with the difference that it uses two neural networks to approximate both the the solution and its gradient.</p><h2 id="References"><a class="docs-heading-anchor" href="#References">References</a><a id="References-1"></a><a class="docs-heading-anchor-permalink" href="#References" title="Permalink"></a></h2><ul><li>Han, J., Jentzen, A., E, W., Solving high-dimensional partial differential equations using deep learning. <a href="https://arxiv.org/abs/1707.02568">arXiv</a> (2018)</li></ul></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../DeepSplitting/">« The <code>DeepSplitting</code> algorithm</a><a class="docs-footer-nextpage" href="../NNStopping/">The <code>NNStopping</code> algorithm »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.3.0 on <span class="colophon-date" title="Monday 8 April 2024 13:14">Monday 8 April 2024</span>. Using Julia version 1.10.2.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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