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Designing Poisson Integrators Through Machine Learning
This paper presents a general method to construct Poisson integrators, i. e., integrators that preserve the underlying Poisson geometry.
Symmetry Preservation in Hamiltonian Systems: Simulation and Learning
This work presents a general geometric framework for simulating and learning the dynamics of Hamiltonian systems that are invariant under a Lie group of transformations.
Convergence-Rate-Matching Discretization of Accelerated Optimization Flows Through Opportunistic State-Triggered Control
A recent body of exciting work seeks to shed light on the behavior of accelerated methods in optimization via high-resolution differential equations.
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