build based on 39540a2

dev/.documenter-siteinfo.json

@@ -1 +1 @@
-{"documenter":{"julia_version":"1.10.4","generation_timestamp":"2024-07-17T09:32:27","documenter_version":"1.5.0"}}
+{"documenter":{"julia_version":"1.10.4","generation_timestamp":"2024-07-29T14:00:44","documenter_version":"1.5.0"}}

dev/DeepBSDE/index.html

@@ -32,7 +32,7 @@
  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/97bd63c6d5d0a4bc4b1487c70cb99da7ea3d6230/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/39540a2f9e6fab1b331a356ecf3d4f505b67a7a5/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;
@@ -49,7 +49,7 @@
  maxiters_limits,
  kwargs...
 ) -&gt; Any
-</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/97bd63c6d5d0a4bc4b1487c70cb99da7ea3d6230/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/39540a2f9e6fab1b331a356ecf3d4f505b67a7a5/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,
@@ -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="#HighDimPDE.DeepBSDE"><code>DeepBSDE</code></a></p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/HighDimPDE.jl/blob/97bd63c6d5d0a4bc4b1487c70cb99da7ea3d6230/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><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.5.0 on <span class="colophon-date" title="Wednesday 17 July 2024 09:32">Wednesday 17 July 2024</span>. Using Julia version 1.10.4.</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="#HighDimPDE.DeepBSDE"><code>DeepBSDE</code></a></p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/HighDimPDE.jl/blob/39540a2f9e6fab1b331a356ecf3d4f505b67a7a5/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><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.5.0 on <span class="colophon-date" title="Monday 29 July 2024 14:00">Monday 29 July 2024</span>. Using Julia version 1.10.4.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>