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Found 6 papers, 4 papers with code
Grokking at the Edge of Linear Separability
Importantly, in the vicinity of the transition, that is, for training sets that are almost separable from the origin, the model may overfit for arbitrarily long times before generalizing.
Grokking in Linear Estimators -- A Solvable Model that Groks without Understanding
Grokking is the intriguing phenomenon where a model learns to generalize long after it has fit the training data.
Charting the Topography of the Neural Network Landscape with Thermal-Like Noise
The training of neural networks is a complex, high-dimensional, non-convex and noisy optimization problem whose theoretical understanding is interesting both from an applicative perspective and for fundamental reasons.
Learned discretizations for passive scalar advection in a 2-D turbulent flow
The computational cost of fluid simulations increases rapidly with grid resolution.
Computational Physics Disordered Systems and Neural Networks Fluid Dynamics
Data-driven discretization: a method for systematic coarse graining of partial differential equations
Many problems in theoretical physics are centered on representing the behavior of a physical theory at long wave lengths and slow frequencies by integrating out degrees of freedom which change rapidly in time and space.
Disordered Systems and Neural Networks Computational Physics
Machine Learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets
Machine learning has gained widespread attention as a powerful tool to identify structure in complex, high-dimensional data.
Soft Condensed Matter
