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<!DOCTYPE html>
<html lang="en">
<head>
<!-- Title -->
<title>About StellarDNN | StellarDNN research Lab</title>
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<h1 class="display-4">StellarDNN</h1>
<p class="lead">StellarDNN is a research lab, led by Pavlos Protopapas, that focuses on using machine learning techniques,
particularly neural networks and transformers, to study and analyze astronomical and physical phenomena.
We use these techniques to classify celestial objects, solve differential equations, and extract important
features from data such as galaxy images or time series. By exploring the intersection of astronomy, physics,
machine learning, and statistics, the group aims to gain a deeper understanding of these phenomena and how
they can be characterized and studied.
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<span class="d-block h3 text-white mb-0">Cosmology</span>
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<span class="d-block display-4 font-size-md-down-4 text-dark text-uppercase mb-0">76+</span>
<span>students</span>
</div>
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<span class="d-block display-4 font-size-md-down-4 text-dark text-uppercase mb-0">400+</span>
<span>publications</span>
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<span class="d-block display-4 font-size-md-down-4 text-dark text-uppercase mb-0">25 </span>
<span>people</span>
</div>
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<h2>Latest Publications</h2>
<p><b> <a href="https://arxiv.org/pdf/2211.00214.pdf"> Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows</b> </a>
<br>R Pellegrin, B Bullwinkel, M Mattheakis, P Protopapas </p>
<p><b><a href="https://arxiv.org/pdf/2212.06965.pdf" > Error-Aware B-PINNs: Improving Uncertainty Quantification in Bayesian Physics-Informed Neural Networks</b> </a>
<br> O Graf, P Flores, P Protopapas, K Pichara</p>
<p><b><a href="https://arxiv.org/pdf/2209.07081">Deqgan: Learning the loss function for pinns with generative adversarial networks</b> </a>
<br>Blake Bullwinkel, Dylan Randle, Pavlos Protopapas, David Sondak</p>
<p><b><a href="https://arxiv.org/pdf/2207.01114.pdf"> Evaluating Error Bound for Physics-Informed Neural Networks on Linear Dynamical Systems</b> </a>
<br> S Liu, X Huang, P Protopapas
</p>
<p><b><a href="https://arxiv.org/pdf/2207.05870"> RcTorch: a PyTorch Reservoir Computing Package with Automated Hyper-Parameter Optimization</b> </a>
<br> Hayden Joy, Marios Mattheakis, Pavlos Protopapas
</p>
<p> </p>
</div>
</div>
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