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index.xml
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<?xml version="1.0" encoding="utf-8" standalone="yes" ?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
<channel>
<title>Onkar Jadhav</title>
<link>https://OnkaraJadhav.github.io/</link>
<atom:link href="https://OnkaraJadhav.github.io/index.xml" rel="self" type="application/rss+xml" />
<description>Onkar Jadhav</description>
<generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><lastBuildDate>Tue, 03 Aug 2021 00:00:00 +0000</lastBuildDate>
<image>
<url>https://OnkaraJadhav.github.io/media/icon_hu0b7a4cb9992c9ac0e91bd28ffd38dd00_9727_512x512_fill_lanczos_center_3.png</url>
<title>Onkar Jadhav</title>
<link>https://OnkaraJadhav.github.io/</link>
</image>
<item>
<title>Complexity reduction for parametric high dimensional models in the analysis of financial risk</title>
<link>https://OnkaraJadhav.github.io/publication/conferenceecmi/</link>
<pubDate>Tue, 03 Aug 2021 00:00:00 +0000</pubDate>
<guid>https://OnkaraJadhav.github.io/publication/conferenceecmi/</guid>
<description></description>
</item>
<item>
<title>Neural network and reduced order modeling for financial risk analysis</title>
<link>https://OnkaraJadhav.github.io/publication/ml/</link>
<pubDate>Tue, 03 Aug 2021 00:00:00 +0000</pubDate>
<guid>https://OnkaraJadhav.github.io/publication/ml/</guid>
<description></description>
</item>
<item>
<title>My Ph.D. Life</title>
<link>https://OnkaraJadhav.github.io/post/getting-started/</link>
<pubDate>Tue, 29 Jun 2021 00:00:00 +0000</pubDate>
<guid>https://OnkaraJadhav.github.io/post/getting-started/</guid>
<description><h2 id="overview">Overview</h2>
<p>My experience as a Marie Curie fellow: <a href="https://www.romsoc.eu/my-ph-d-life/" target="_blank" rel="noopener">https://www.romsoc.eu/my-ph-d-life/</a></p>
</description>
</item>
<item>
<title>Parametric Model Order Reduction with Adaptive Greedy Sampling</title>
<link>https://OnkaraJadhav.github.io/post/getting-started-copy-2/</link>
<pubDate>Tue, 29 Jun 2021 00:00:00 +0000</pubDate>
<guid>https://OnkaraJadhav.github.io/post/getting-started-copy-2/</guid>
<description><h2 id="overview">Overview</h2>
<p>My experience as a Marie Curie fellow: <a href="https://youtu.be/rJAUDG6Y0Pc" target="_blank" rel="noopener">https://youtu.be/rJAUDG6Y0Pc</a></p>
</description>
</item>
<item>
<title>A multi-fidelity wind surface pressure assessment via machine learning: A high-rise building case</title>
<link>https://OnkaraJadhav.github.io/publication/articled4w/</link>
<pubDate>Thu, 03 Jun 2021 00:00:00 +0000</pubDate>
<guid>https://OnkaraJadhav.github.io/publication/articled4w/</guid>
<description></description>
</item>
<item>
<title>Error analysis of a model order reduction framework for financial risk analysis</title>
<link>https://OnkaraJadhav.github.io/publication/articleetna/</link>
<pubDate>Thu, 03 Jun 2021 00:00:00 +0000</pubDate>
<guid>https://OnkaraJadhav.github.io/publication/articleetna/</guid>
<description></description>
</item>
<item>
<title>Model order reduction for the simulation of parametric interest rate models in financial risk analysis</title>
<link>https://OnkaraJadhav.github.io/publication/articlejmi/</link>
<pubDate>Thu, 03 Jun 2021 00:00:00 +0000</pubDate>
<guid>https://OnkaraJadhav.github.io/publication/articlejmi/</guid>
<description></description>
</item>
<item>
<title>ECMI 2021 conference</title>
<link>https://OnkaraJadhav.github.io/project/ecmi2021/</link>
<pubDate>Thu, 01 Apr 2021 00:00:00 +0000</pubDate>
<guid>https://OnkaraJadhav.github.io/project/ecmi2021/</guid>
<description></description>
</item>
<item>
<title>Graduate seminar, Numerical mathematics</title>
<link>https://OnkaraJadhav.github.io/project/tubseminar2020/</link>
<pubDate>Thu, 01 Apr 2021 00:00:00 +0000</pubDate>
<guid>https://OnkaraJadhav.github.io/project/tubseminar2020/</guid>
<description></description>
</item>
<item>
<title>Graduate seminar, Numerical mathematics</title>
<link>https://OnkaraJadhav.github.io/project/tubseminar2021/</link>
<pubDate>Thu, 01 Apr 2021 00:00:00 +0000</pubDate>
<guid>https://OnkaraJadhav.github.io/project/tubseminar2021/</guid>
<description></description>
</item>
<item>
<title>Machine learning and deep learning for CFD</title>
<link>https://OnkaraJadhav.github.io/project/data4wind/</link>
<pubDate>Thu, 01 Apr 2021 00:00:00 +0000</pubDate>
<guid>https://OnkaraJadhav.github.io/project/data4wind/</guid>
<description><p>Computational fluid dynamics (CFD) represents an attractive tool for estimating wind pressures and wind loads on high-rise buildings. The CFD analyses can be conducted either by low-fidelity simulations (RANS) or by high-fidelity ones (LES). The low-fidelity model can efficiently estimate wind pressures over a large range of wind directions, but it generally lacks accuracy. On the other hand, the high-fidelity model generally exhibits satisfactory accuracy, yet, the high computational cost can limit the number of approaching wind angles that can be considered. In order to take advantage of the main benefits of these two CFD approaches, a multi-fidelity machine learning framework is investigated that aims to ensure the simulation accuracy while maintaining the computational efficiency.</p>
</description>
</item>
<item>
<title>Model Order Reduction for Finance</title>
<link>https://OnkaraJadhav.github.io/project/morfinance/</link>
<pubDate>Thu, 01 Apr 2021 00:00:00 +0000</pubDate>
<guid>https://OnkaraJadhav.github.io/project/morfinance/</guid>
<description><p>It is essential to be aware of the financial risk associated with an invested product. The risk analysis of financial instruments often requires the valuation of such instruments under a wide range of future market scenarios. The market scenarios (e.g., interest rates) are then input parameters in a valuation function that delivers the fair value of such financial instruments. These models are calibrated based on market scenarios that generate a high-dimensional parameter space. In short, to perform the risk analysis, the financial model needs to be solved for such a high dimensional parameter space, and this requires efficient algorithms. These two benchmark cases present the model order reduction approach based on the proper orthogonal decomposition approach with greedy sampling approaches for parameter sampling. The first case generates the 10000 simulated yield curves, which are then used to calibrate the financial model parameters. The second case presents both the classical and adaptive greedy sampling approaches.</p>
<p>In the source directory, one can find all source files required to run the benchmark cases. The directory benchmark contains the input data with the executable files. The Benchmark1_1.m file executes the yield curve simulation while the Benchmark1_2.nb file runs the parameter calibration. The classical greedy and adaptive greedy sampling techniques can be executed using Benchmark2_1.m and Benchmark2_2.m files. One can find a PDf file with a detailed step-by-step description of the benchmark case in the directory documentation.</p>
</description>
</item>
<item>
<title>Workshop in Industrial Mathematics 2019</title>
<link>https://OnkaraJadhav.github.io/project/wim2019/</link>
<pubDate>Thu, 01 Apr 2021 00:00:00 +0000</pubDate>
<guid>https://OnkaraJadhav.github.io/project/wim2019/</guid>
<description></description>
</item>
<item>
<title>Model order reduction for Financial risk analysis, blog post</title>
<link>https://OnkaraJadhav.github.io/post/getting-started-copy/</link>
<pubDate>Wed, 22 Jul 2020 00:00:00 +0000</pubDate>
<guid>https://OnkaraJadhav.github.io/post/getting-started-copy/</guid>
<description><h2 id="overview">Overview</h2>
<p>Model Order Reduction in Computational Finance: <a href="https://www.romsoc.eu/model-hierarchy-for-the-reduced-order-modelling/" target="_blank" rel="noopener">https://www.romsoc.eu/model-hierarchy-for-the-reduced-order-modelling/</a></p>
</description>
</item>
<item>
<title>Error Analysis of a Model Order Reduction Framework for Financial Risk Analysis</title>
<link>https://OnkaraJadhav.github.io/publication/preprint2/</link>
<pubDate>Thu, 27 Feb 2020 00:00:00 +0000</pubDate>
<guid>https://OnkaraJadhav.github.io/publication/preprint2/</guid>
<description></description>
</item>
<item>
<title>Model order reduction for parametric high dimensional models in the analysis of financial risk</title>
<link>https://OnkaraJadhav.github.io/publication/preprint/</link>
<pubDate>Thu, 27 Feb 2020 00:00:00 +0000</pubDate>
<guid>https://OnkaraJadhav.github.io/publication/preprint/</guid>
<description></description>
</item>
<item>
<title>Slides</title>
<link>https://OnkaraJadhav.github.io/slides/example/</link>
<pubDate>Tue, 05 Feb 2019 00:00:00 +0000</pubDate>
<guid>https://OnkaraJadhav.github.io/slides/example/</guid>
<description><h1 id="create-slides-in-markdown-with-wowchemy">Create slides in Markdown with Wowchemy</h1>
<p><a href="https://wowchemy.com/" target="_blank" rel="noopener">Wowchemy</a> | <a href="https://owchemy.com/docs/managing-content/#create-slides" target="_blank" rel="noopener">Documentation</a></p>
<hr>
<h2 id="features">Features</h2>
<ul>
<li>Efficiently write slides in Markdown</li>
<li>3-in-1: Create, Present, and Publish your slides</li>
<li>Supports speaker notes</li>
<li>Mobile friendly slides</li>
</ul>
<hr>
<h2 id="controls">Controls</h2>
<ul>
<li>Next: <code>Right Arrow</code> or <code>Space</code></li>
<li>Previous: <code>Left Arrow</code></li>
<li>Start: <code>Home</code></li>
<li>Finish: <code>End</code></li>
<li>Overview: <code>Esc</code></li>
<li>Speaker notes: <code>S</code></li>
<li>Fullscreen: <code>F</code></li>
<li>Zoom: <code>Alt + Click</code></li>
<li><a href="https://github.com/hakimel/reveal.js#pdf-export" target="_blank" rel="noopener">PDF Export</a>: <code>E</code></li>
</ul>
<hr>
<h2 id="code-highlighting">Code Highlighting</h2>
<p>Inline code: <code>variable</code></p>
<p>Code block:</p>
<pre><code class="language-python">porridge = &quot;blueberry&quot;
if porridge == &quot;blueberry&quot;:
print(&quot;Eating...&quot;)
</code></pre>
<hr>
<h2 id="math">Math</h2>
<p>In-line math: $x + y = z$</p>
<p>Block math:</p>
<p>$$
f\left( x \right) = ;\frac{{2\left( {x + 4} \right)\left( {x - 4} \right)}}{{\left( {x + 4} \right)\left( {x + 1} \right)}}
$$</p>
<hr>
<h2 id="fragments">Fragments</h2>
<p>Make content appear incrementally</p>
<pre><code>{{% fragment %}} One {{% /fragment %}}
{{% fragment %}} **Two** {{% /fragment %}}
{{% fragment %}} Three {{% /fragment %}}
</code></pre>
<p>Press <code>Space</code> to play!</p>
<p><span class="fragment " >
One
</span>
<span class="fragment " >
<strong>Two</strong>
</span>
<span class="fragment " >
Three
</span></p>
<hr>
<p>A fragment can accept two optional parameters:</p>
<ul>
<li><code>class</code>: use a custom style (requires definition in custom CSS)</li>
<li><code>weight</code>: sets the order in which a fragment appears</li>
</ul>
<hr>
<h2 id="speaker-notes">Speaker Notes</h2>
<p>Add speaker notes to your presentation</p>
<pre><code class="language-markdown">{{% speaker_note %}}
- Only the speaker can read these notes
- Press `S` key to view
{{% /speaker_note %}}
</code></pre>
<p>Press the <code>S</code> key to view the speaker notes!</p>
<aside class="notes">
<ul>
<li>Only the speaker can read these notes</li>
<li>Press <code>S</code> key to view</li>
</ul>
</aside>
<hr>
<h2 id="themes">Themes</h2>
<ul>
<li>black: Black background, white text, blue links (default)</li>
<li>white: White background, black text, blue links</li>
<li>league: Gray background, white text, blue links</li>
<li>beige: Beige background, dark text, brown links</li>
<li>sky: Blue background, thin dark text, blue links</li>
</ul>
<hr>
<ul>
<li>night: Black background, thick white text, orange links</li>
<li>serif: Cappuccino background, gray text, brown links</li>
<li>simple: White background, black text, blue links</li>
<li>solarized: Cream-colored background, dark green text, blue links</li>
</ul>
<hr>
<section data-noprocess data-shortcode-slide
data-background-image="/media/boards.jpg"
>
<h2 id="custom-slide">Custom Slide</h2>
<p>Customize the slide style and background</p>
<pre><code class="language-markdown">{{&lt; slide background-image=&quot;/media/boards.jpg&quot; &gt;}}
{{&lt; slide background-color=&quot;#0000FF&quot; &gt;}}
{{&lt; slide class=&quot;my-style&quot; &gt;}}
</code></pre>
<hr>
<h2 id="custom-css-example">Custom CSS Example</h2>
<p>Let&rsquo;s make headers navy colored.</p>
<p>Create <code>assets/css/reveal_custom.css</code> with:</p>
<pre><code class="language-css">.reveal section h1,
.reveal section h2,
.reveal section h3 {
color: navy;
}
</code></pre>
<hr>
<h1 id="questions">Questions?</h1>
<p><a href="https://github.com/wowchemy/wowchemy-hugo-modules/discussions" target="_blank" rel="noopener">Ask</a></p>
<p><a href="https://wowchemy.com/docs/managing-content/#create-slides" target="_blank" rel="noopener">Documentation</a></p>
</description>
</item>
<item>
<title>Parametric model order reduction of a thermoelectric generator for electrically active implants</title>
<link>https://OnkaraJadhav.github.io/publication/confpapermsc2/</link>
<pubDate>Sun, 15 Apr 2018 00:00:00 +0000</pubDate>
<guid>https://OnkaraJadhav.github.io/publication/confpapermsc2/</guid>
<description></description>
</item>
<item>
<title>Design of a thermoelectric generator for electrical active implants</title>
<link>https://OnkaraJadhav.github.io/publication/confpapermsc1/</link>
<pubDate>Mon, 23 Oct 2017 00:00:00 +0000</pubDate>
<guid>https://OnkaraJadhav.github.io/publication/confpapermsc1/</guid>
<description></description>
</item>
<item>
<title></title>
<link>https://OnkaraJadhav.github.io/admin/config.yml</link>
<pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
<guid>https://OnkaraJadhav.github.io/admin/config.yml</guid>
<description></description>
</item>
</channel>
</rss>