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Found 5 papers, 2 papers with code
Machine Learning for Discovering Effective Interaction Kernels between Celestial Bodies from Ephemerides
Building accurate and predictive models of the underlying mechanisms of celestial motion has inspired fundamental developments in theoretical physics.
Learning Interaction Kernels for Agent Systems on Riemannian Manifolds
Interacting agent and particle systems are extensively used to model complex phenomena in science and engineering.
Learning Theory for Inferring Interaction Kernels in Second-Order Interacting Agent Systems
Modeling the complex interactions of systems of particles or agents is a fundamental scientific and mathematical problem that is studied in diverse fields, ranging from physics and biology, to economics and machine learning.
Data-driven Discovery of Emergent Behaviors in Collective Dynamics
We consider the fundamental problem of inferring interaction kernels from observations of agent-based dynamical systems given observations of trajectories, in particular for collective dynamical systems exhibiting emergent behaviors with complicated interaction kernels, in a nonparametric fashion, and for kernels which are parametrized by a single unknown parameter.
Quantum Loewner Evolution
We propose QLE$(2, 1)$ as a scaling limit for DLA on a random spanning-tree-decorated planar map, and QLE$(8/3, 0)$ as a scaling limit for the Eden model on a random triangulation.
Probability Mathematical Physics Complex Variables Mathematical Physics
