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assets/jupyter/blog.ipynb.html

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feed.xml

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<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" xml:lang="en"><generator uri="https://jekyllrb.com/" version="4.3.3">Jekyll</generator><link href="https://kgml-lab.github.io/feed.xml" rel="self" type="application/atom+xml"/><link href="https://kgml-lab.github.io/" rel="alternate" type="text/html" hreflang="en"/><updated>2025-04-20T19:23:40+00:00</updated><id>https://kgml-lab.github.io/feed.xml</id><title type="html">KGML Lab</title><subtitle>Group website of Knowledge-guided Machine Learning (KGML) Lab at Virginia Tech </subtitle><entry><title type="html">Google Gemini updates: Flash 1.5, Gemma 2 and Project Astra</title><link href="https://kgml-lab.github.io/blog/2024/google-gemini-updates-flash-15-gemma-2-and-project-astra/" rel="alternate" type="text/html" title="Google Gemini updates: Flash 1.5, Gemma 2 and Project Astra"/><published>2024-05-14T00:00:00+00:00</published><updated>2024-05-14T00:00:00+00:00</updated><id>https://kgml-lab.github.io/blog/2024/google-gemini-updates-flash-15-gemma-2-and-project-astra</id><content type="html" xml:base="https://kgml-lab.github.io/blog/2024/google-gemini-updates-flash-15-gemma-2-and-project-astra/"><![CDATA[]]></content><author><name></name></author><summary type="html"><![CDATA[We’re sharing updates across our Gemini family of models and a glimpse of Project Astra, our vision for the future of AI assistants.]]></summary></entry><entry><title type="html">Displaying External Posts on Your al-folio Blog</title><link href="https://kgml-lab.github.io/blog/2022/displaying-external-posts-on-your-al-folio-blog/" rel="alternate" type="text/html" title="Displaying External Posts on Your al-folio Blog"/><published>2022-04-23T23:20:09+00:00</published><updated>2022-04-23T23:20:09+00:00</updated><id>https://kgml-lab.github.io/blog/2022/displaying-external-posts-on-your-al-folio-blog</id><content type="html" xml:base="https://kgml-lab.github.io/blog/2022/displaying-external-posts-on-your-al-folio-blog/"><![CDATA[]]></content><author><name></name></author></entry></feed>
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<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" xml:lang="en"><generator uri="https://jekyllrb.com/" version="4.3.3">Jekyll</generator><link href="https://kgml-lab.github.io/feed.xml" rel="self" type="application/atom+xml"/><link href="https://kgml-lab.github.io/" rel="alternate" type="text/html" hreflang="en"/><updated>2025-04-20T19:47:08+00:00</updated><id>https://kgml-lab.github.io/feed.xml</id><title type="html">KGML Lab</title><subtitle>Group website of Knowledge-guided Machine Learning (KGML) Lab at Virginia Tech </subtitle><entry><title type="html">Google Gemini updates: Flash 1.5, Gemma 2 and Project Astra</title><link href="https://kgml-lab.github.io/blog/2024/google-gemini-updates-flash-15-gemma-2-and-project-astra/" rel="alternate" type="text/html" title="Google Gemini updates: Flash 1.5, Gemma 2 and Project Astra"/><published>2024-05-14T00:00:00+00:00</published><updated>2024-05-14T00:00:00+00:00</updated><id>https://kgml-lab.github.io/blog/2024/google-gemini-updates-flash-15-gemma-2-and-project-astra</id><content type="html" xml:base="https://kgml-lab.github.io/blog/2024/google-gemini-updates-flash-15-gemma-2-and-project-astra/"><![CDATA[]]></content><author><name></name></author><summary type="html"><![CDATA[We’re sharing updates across our Gemini family of models and a glimpse of Project Astra, our vision for the future of AI assistants.]]></summary></entry><entry><title type="html">Displaying External Posts on Your al-folio Blog</title><link href="https://kgml-lab.github.io/blog/2022/displaying-external-posts-on-your-al-folio-blog/" rel="alternate" type="text/html" title="Displaying External Posts on Your al-folio Blog"/><published>2022-04-23T23:20:09+00:00</published><updated>2022-04-23T23:20:09+00:00</updated><id>https://kgml-lab.github.io/blog/2022/displaying-external-posts-on-your-al-folio-blog</id><content type="html" xml:base="https://kgml-lab.github.io/blog/2022/displaying-external-posts-on-your-al-folio-blog/"><![CDATA[]]></content><author><name></name></author></entry></feed>

projects/GFI-framework/index.html

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<h2 class="title is-3"> Abstract </h2>
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In subsurface imaging, learning the mapping from velocity maps to seismic waveforms (forward problem) and waveforms to velocity (inverse problem) is important for several applications. While traditional techniques for solving forward and inverse problems are computationally prohibitive, there is a growing interest in leveraging recent advances in deep learning to learn the mapping between velocity maps and seismic waveform images directly from data.
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Despite the variety of architectures explored in previous works, several open questions remain unanswered such as the effect of latent space sizes, the importance of manifold learning, the complexity of translation models, and the value of jointly solving forward and inverse problems.
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<h2 class="title is-3">Experiments</h2>
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projects/GFI-framework/static/css/index.css

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sitemap.xml

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