82 citations · 200 across the 10 of their papers we have counts for
11 papers
Learning Schwarzschild Geodesic Motion with Lagrangian Neural Networks
Sukru Caglar, Sena Kalabalik, Abdullah Umut Hamzaogullari +1
Lagrangian Neural Networks (LNNs) learn dynamics from trajectory data by parameterizing a scalar Lagrangian and deriving accelerations through the Euler--Lagrange equation, providi…
Blind Recovery of Latent Domains via Unsupervised Symmetry Discovery
Onur Efe, Arkadas Ozakin
Primary motivation in blind inverse problems is to recover signals of interest from corrupted observations without knowing the obfuscating mechanism. Blind deconvolution is a promi…
Learning Relativistic Geodesics and Chaotic Dynamics via Stabilized Lagrangian Neural Networks
Abdullah Umut Hamzaogullari, Arkadas Ozakin
Lagrangian Neural Networks (LNNs) can learn arbitrary Lagrangians from trajectory data, but their unusual optimization objective leads to significant training instabilities that li…
SymmetryLens: Unsupervised Symmetry Learning via Locality and Density Preservation
Onur Efe, Arkadas Ozakin
We develop a new unsupervised symmetry learning method that starts with raw data and provides the minimal generator of an underlying Lie group of symmetries, together with a symmet…
RECOVAR: Representation Covariances on Deep Latent Spaces for Seismic Event Detection
Onur Efe, Arkadas Ozakin
While modern deep learning methods have shown great promise in the problem of earthquake detection, the most successful methods so far have been based on supervised learning, which…
Controlling trapping potentials and stray electric fields in a microfabricated ion trap through design and compensation
S. Charles Doret, Jason M. Amini, Kenneth Wright +8
Recent advances in quantum information processing with trapped ions have demonstrated the need for new ion trap architectures capable of holding and manipulating chains of many (>1…