StellarDNN
StellarDNN is a research lab at the School of Engineering and Applied Sciences (SEAS), 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.
If you are seeking information on current research projects, please view the list of open projects here.
Latest Publications
Gravitational duals from equations of state
Bea, Y., Jiménez, R., Mateos, D., Liu, S., Protopapas, P., Tarancón-Álvarez, P., Tejerina-Pérez, P.
Behavioral Malware Detection using a Language Model Classifier Trained on sys2vec Embeddings
John Carter, Spiros Mancoridis, Pavlos Protopapas, Erick Galinkin
Faster Bayesian inference with neural network bundles and new results for ΛCDM models
A.T. Chantada, S.J. Landau, P. Protopapas, C.G. Scóccola
Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows
R Pellegrin, B Bullwinkel, M Mattheakis, P Protopapas
Error-Aware B-PINNs: Improving Uncertainty Quantification in Bayesian Physics-Informed Neural Networks
O Graf, P Flores, P Protopapas, K Pichara