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Found 7 papers, 0 papers with code
Towards a Phenomenological Understanding of Neural Networks: Data
This framework is then applied to the problem of optimal data selection for the training of NNs.
A duality connecting neural network and cosmological dynamics
We demonstrate that the dynamics of neural networks trained with gradient descent and the dynamics of scalar fields in a flat, vacuum energy dominated Universe are structurally profoundly related.
Improving Simulations with Symmetry Control Neural Networks
The dynamics of physical systems is often constrained to lower dimensional sub-spaces due to the presence of conserved quantities.
Symmetry Control Neural Networks
We show that such coordinates can be searched for automatically with appropriate loss functions which naturally arise from Hamiltonian dynamics.
Detecting Symmetries with Neural Networks
Identifying symmetries in data sets is generally difficult, but knowledge about them is crucial for efficient data handling.
Connecting Dualities and Machine Learning
Dualities are widely used in quantum field theories and string theory to obtain correlation functions at high accuracy.
GANs for generating EFT models
In this case, the machine knows consistent examples of supersymmetric field theories with a single field and generates new examples of such theories.
