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Found 3 papers, 1 papers with code
Random Feature Models for Learning Interacting Dynamical Systems
Particle dynamics and multi-agent systems provide accurate dynamical models for studying and forecasting the behavior of complex interacting systems.
Extending the step-size restriction for gradient descent to avoid strict saddle points
We provide larger step-size restrictions for which gradient descent based algorithms (almost surely) avoid strict saddle points.
Sparse model selection via integral terms
Model selection and parameter estimation are important for the effective integration of experimental data, scientific theory, and precise simulations.
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