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<!DOCTYPE html>
<html>
<head>
<title>Predictive Science Laboratory</title>
<link rel="stylesheet" type="text/css" href="styles.css">
</head>
<body>
<h1>Predictive Science Laboratory</h1>
<h2>About Us</h2>
<p>We are a research laboratory at the <a href="https://engineering.purdue.edu/ME">School of Mechanical Engineering</a> of <a href="https://www.purdue.edu">Purdue University</a>, founded in 2014 by Dr. <a href="#bilionis">Ilias Bilionis</a>.</p>
<h2>Mission</h2>
<p>Our mission is to develop scientific machine learning technologies to accelerate engineering innovation.</p>
<h2>Philosophy</h2>
<p>We operate at the intersection of mathematics, statistics, and engineering, facilitating communication between these disciplines by employing Bayesian probability and an additional layer of causality expressed through differential equations.</p>
<h2>Basic Research Areas</h2>
<ul>
<li>Uncertainty propagation through high-dimensional stochastic differential equations:
e.g., <a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=rjXLtJMAAAAJ&citation_for_view=rjXLtJMAAAAJ:NxmKEeNBbOMC">(Tripathy et al., 2016)</a>,
<a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=rjXLtJMAAAAJ&citation_for_view=rjXLtJMAAAAJ:X0DADzN9RKwC">(Karumuri et al., 2020)</a>.
</li>
<li>Bayesian inverse problems:
e.g., <a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=rjXLtJMAAAAJ&citation_for_view=rjXLtJMAAAAJ:_FxGoFyzp5QC">(Bilionis et al., 2013)</a>,
<a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=rjXLtJMAAAAJ&sortby=pubdate&citation_for_view=rjXLtJMAAAAJ:rLGzs9wiiwIC">(Karumuri et al., 2023)</a>.
</li>
<li>Sequential design of experiments:
e.g., <a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=rjXLtJMAAAAJ&cstart=20&pagesize=80&citation_for_view=rjXLtJMAAAAJ:z6xuaG2dYH0C">(Pandita et al., 2016)</a>,
<a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=rjXLtJMAAAAJ&cstart=20&pagesize=80&citation_for_view=rjXLtJMAAAAJ:EaFouW7jFu4C">(Pandita et al., 2016)</a>.
</li>
<li>Information field theory as a unified paradigm for uncertainty quantification. This is where most of our efforts are at the moment.
To learn more read the papers:
<a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=rjXLtJMAAAAJ&sortby=pubdate&citation_for_view=rjXLtJMAAAAJ:natZJ_-F0IUC">(Alberts et al., 2023)</a>
<a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=rjXLtJMAAAAJ&sortby=pubdate&citation_for_view=rjXLtJMAAAAJ:9shLKfS_uJEC">(Hao et al., 2024)</a>.
Or watch this video from a recent talk at the Isaac Newton Institute for Mathematical Sciences in Cambridge, UK:
<br>
<iframe width="640" height="360" src="https://www.youtube.com/embed/DXbk3XcRD9Q" title="Associate Prof. Ilias Bilionis | A unifying paradigm for bringing together multimodal data and..." frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
</li>
</ul>
<h2>Current Projects</h2>
<ul>
<li>
Bayesian hemodynamic flow reconstruction from imaging modalities using information field theory (NSF).
</li>
<li>
Subcutaneous and intrathecal drug delivery (Eli Lilly): e.g., <a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=rjXLtJMAAAAJ&sortby=pubdate&citation_for_view=rjXLtJMAAAAJ:KTwcwpFFj4wC">(de Lucio et al., 2023)</a>,
<a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=rjXLtJMAAAAJ&sortby=pubdate&citation_for_view=rjXLtJMAAAAJ:PlWzFYVEG4EC">(Sree et al., 2023)</a>.
</li>
<li>
Electric machines (Ford): e.g., <a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=rjXLtJMAAAAJ&sortby=pubdate&citation_for_view=rjXLtJMAAAAJ:Y0-TYkg6YM4C">(Beltrán-Pulido et al., 2022)</a>,
<a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=rjXLtJMAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=rjXLtJMAAAAJ:pYKElYtJMmwC">(Beltrán-Pulido et al., 2020)</a>.
</li>
<li>
Design of corrosion-resistant high-entropy alloys (NSF): e.g.,
<a href="https://www.sciencedirect.com/science/article/abs/pii/S0927025622005626">(Karumuri et al., 2023)</a>
</li>
<li>
Thermal insulation for hypersonic vehicles (AFRL): e.g., <a href="https://www.sciencedirect.com/science/article/abs/pii/S0045782523007831">(Thomas et al., 2024)</a>,
<a href="https://www.sciencedirect.com/science/article/abs/pii/S0021999124003668">(Karumuri et al., 2024)</a>.
</li>
<li> Uncertainty quantification in combustions modeling (Cummins): e.g., <a href="https://www.sciencedirect.com/science/article/pii/S2405896322028233">(Zinage et al., 2022)</a>.
</li>
<li>
Smart buildings (NSF): e.g., <a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=rjXLtJMAAAAJ&sortby=pubdate&citation_for_view=rjXLtJMAAAAJ:TesyEGJKHF4C">(Kim et al., 2022)</a>,
<a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=rjXLtJMAAAAJ&sortby=pubdate&citation_for_view=rjXLtJMAAAAJ:YPNY0knpFBYC">(Kim et al., 2023)</a>.
</li>
<li>
Extra-terrestrial habitats (NASA): e.g., <a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=rjXLtJMAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=rjXLtJMAAAAJ:oTdOBqtIf_kC">(Dyke et al., 2021)</a>,
<a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=rjXLtJMAAAAJ&cstart=20&pagesize=80&sortby=pubdate&citation_for_view=rjXLtJMAAAAJ:KS-xo-ZNxMsC">(Maghareh et al., 2021)</a>
</li>
</ul>
<h2>Funding</h2>
<p>Our funding comes from NSF, NASA, DARPA, AFRL, Eli Lilly, Cummins, and Ford.</p>
<h2>Teaching</h2>
<p>Bilionis loves teaching scientific machine learning, probabilistic thinking, and uncertainty quantification to engineers. Some examples are these:
<ul>
<li><a href="https://github.com/PredictiveScienceLab/advanced-scientific-machine-learning">ME 697 - Advanced Scientific Machine Learning</a> is offered on-campus during Spring 2024. You can find the online textbook <a href="https://predictivesciencelab.github.io/advanced-scientific-machine-learning/intro.html">here</a>. But it is a work in progress.</li>
<li><a href="https://github.com/PredictiveScienceLab/data-analytics-se">ME 539 - Introduction to Scientific Machine Learning</a> is being offered during Spring 2024 both online. You can find the online textbook <a href="https://predictivesciencelab.github.io/data-analytics-se/index.html">here</a>.</li>
</li>
<li><a href="https://github.com/PurdueMechanicalEngineering/me-239-intro-to-data-science">ME 239 - Introduction to Data Science for Mechanical Engineers</a>. This was offered for the last time during Spring 2023. You can find the online textbook <a href="https://purduemechanicalengineering.github.io/me-239-intro-to-data-science/index.html">here</a>.</li>
<li><a href="https://github.com/PredictiveScienceLab/uq-course">ME 597 - Introduction to Uncertainty Quantification</a>. Bilionis taught this for the last time in Spring 2020. About one third of the topics taught there made it into ME 539. However, this course contains some classical uncertainty quantification lectures that Bilionis could not fit in the new course. You an find the video lectures <a href="https://nanohub.org/resources/27789">here</a>.</li>
<li><a href="https://youtu.be/o9JaZGWekWQ">A hands-on introduction to physics-informed machine learning</a>. This is a short lecture on the topic. You can find the Jupyter notebook <a href="https://nanohub.org/tools/handsonpinns">here</a>.</li>
</ul>
</p>
<h2>People</h2>
<div class="member">
<div class="image">
<img src="pics/bilionis.jpeg" alt="Ilias Bilionis" width="200" height="200">
</div>
<div class="info">
<h3 id="bilionis">Ilias Bilionis</h3>
<p>Professor of Mechanical Engineering</p>
<p>Purdue University</p>
<p>[email protected]</p>
<p><a href="https://scholar.google.com/citations?user=rjXLtJMAAAAJ&hl=en">Google Scholar</a></p>
<p><a href="docs/bilionis_cv.pdf">CV</a></p>
<p><a href="docs/bilionis_bio.html">Short bio</a></p>
<p><a href="https://github.com/ebilionis">Personal GitHub profile</a></p>
<p><a href="https://github.com/PredictiveScienceLab">Lab GitHub</a></p>
</div>
</div>
<div class="member">
<div class="image-small">
<img src="pics/akshay.jpg" alt="Akshay Jacob Thomas">
</div>
<div class="info">
<h3 id="akshay"> Akshay Jacob Thomas</h3>
<p> Postdoctoral Researcher</p>
<p> Bayesian inverse problems, Physics informed neural networks, Digital twins for advanced manufacturing</p>
<p><a href="https://scholar.google.com/citations?user=lWoiOrEAAAAJ&hl=en">Google Scholar</a></p>
<p><a href="https://www.linkedin.com/in/akshayjacobthomas/">Linkedin</a></p>
</div>
</div>
<div class="member">
<div class="image-small">
<img src="pics/alex.jpg" alt="Alexander Alberts">
</div>
<div class="info">
<h3>Alexander Alberts</h3>
<p>Postdoctoral Researcher</p>
<p>Physics-informed, information field theory</p>
</div>
</div>
<div class="member">
<div class="image-small">
<img src="pics/kairui.jpg" alt="Kairui Hao">
</div>
<div class="info">
<h3>Kairui Hao</h3>
<p>Postdoctoral Researcher</p>
<p>Physics-informed, information field theory for dynamical systems</p>
</div>
</div>
<div class="member">
<div class="image-small">
<img src="pics/atharva.jpg" alt="Atharva Hans">
</div>
<div class="info">
<h3 id=“hans”>Atharva Hans</h3>
<p>Postdoctoral Researcher</p>
<p>Superresolution of 4D flow MRI using information field theory</p>
<p>[email protected]</p>
<p><a href="https://scholar.google.com/citations?user=ZgRThEcAAAAJ&hl=en&oi=ao">Google Scholar</a></p>
<p><a href="https://github.com/gg2uah">Personal GitHub profile</a></p>
</div>
</div>
<div class="member">
<div class="image-small">
<img src="pics/andres.jpg" alt="Andres Felipe Beltran-Pulido">
</div>
<div class="info">
<h3 id="beltranp">Andrés Felipe Beltrán-Pulido</h3>
<p>Ph.D. Student</p>
<p>Purdue University</p>
<p>[email protected]</p>
<p> Electric machine design optimization using physics informed neural networks</p>
<p><a href="https://scholar.google.com/citations?user=GlDZbukAAAAJ&hl=en">Google Scholar</a></p>
<p><a href="https://github.com/afbeltranp">Personal GitHub profile</a></p>
</div>
</div>
<div class="member">
<div class="image-small">
<img src="pics/rudra.jpg" alt="Rudra Sethu">
</div>
<div class="info">
<h3>Rudra Sethu Viji</h3>
<p>Ph.D. Student</p>
<p>Information field theory for fluid flow reconstruction from non-intrusive flow measurements</p>
</div>
</div>
<div class="member">
<div class="image-small">
<img src="pics/sreehari.jpg" alt="Sreehari Manikkan">
</div>
<div class="info">
<h3>Sreehari Manikkan</h3>
<p>Ph.D. Student</p>
<p>Digital twins for smart buildings</p>
</div>
</div>
<div class="member">
<div class="image-small">
<img src="pics/wesley.jpg" alt="Wesley Holt">
</div>
<div class="info">
<h3>Wesley Holt</h3>
<p>Ph.D. Student</p>
<p>Information field theory</p>
</div>
</div>
<div class="member">
<div class="image-small">
<img src="pics/shrenik.jpg" alt="Shrenik Zinage Vijaykumar">
</div>
<div class="info">
<h3>Shrenik Vijaykumar Zinage</h3>
<p>Ph.D. Student</p>
<p>Emissions modeling</p>
<p>[email protected]</p>
<p><a href="https://scholar.google.com/citations?user=CuaVvXsAAAAJ&hl=en">Google Scholar</a></p>
<p><a href="https://www.researchgate.net/profile/Shrenik-Zinage">ResearchGate</a></p>
<p><a href="https://www.linkedin.com/in/shrenik-zinage-1a727a157/">LinkedIn</a></p>
<p><a href="https://github.com/shrenikvz">GitHub</a></p>
</div>
</div>
<div class="member">
<div class="image-small">
<img src="pics/max.jpeg" alt="Maxwell Bolt">
</div>
<div class="info">
<h3>Maxwell Bolt</h3>
<p>Ph.D. Student</p>
<p>Emissions modeling</p>
<p><a href="https://www.linkedin.com/in/maxwell-bolt">Linkedin</a></p>
</div>
</div>
<div class="member">
<div class="image-small">
<img src="pics/rohan.jpeg" alt="Rohan Dekate">
</div>
<div class="info">
<h3>Rohan Dekate</h3>
<p>Ph.D. Student</p>
<p>Operator learning</p>
<p><a href="https://www.linkedin.com/in/rohanmdekate/">Linkedin</a></p>
</div>
</div>
</div>
For a list of former members, please visit the <a href="past_members.html">PSL website</a>.
<h2>How to join the group?</h2>
We are continuously looking for qualified people to join the group. If you are interested in working with us, please send me an email with your CV and a brief description of your research interests. I will get back to you as soon as possible.
<h2>About this website</h2>
<p>This website was made in plain html with the help of ChatGPT.
If you want to have fun, you can see the log of our conversation <a href="https://chat.openai.com/share/9efdf0b7-b8bb-4e75-9e3e-ccfa79f06bfe">here</a>.
It was last updated on September 17, 2024.</p>
</body>
</html>