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Found 3 papers, 3 papers with code
Extremal learning: extremizing the output of a neural network in regression problems
Finding the extremizing input of an approximated model is formulated as the training of an additional neural network with a loss function that minimizes when the extremizing input is achieved.
Constraining Fundamental Physics with the Event Horizon Telescope
We show how EHT observations can constrain this reflection coefficient, assuming only that the deviations from GR are small enough to be treated perturbatively.
General Relativity and Quantum Cosmology High Energy Physics - Theory
Improving Statistical Sensitivity of X-ray Searches for Axion-Like Particles
X-ray observations of bright AGNs in or behind galaxy clusters offer unique capabilities to constrain axion-like particles (ALPs).
Cosmology and Nongalactic Astrophysics High Energy Astrophysical Phenomena High Energy Physics - Phenomenology
