SILAS jointly optimizes polynomial ODE vector fields and polynomial Lyapunov functions from data to produce models with provably bounded trajectories via compact absorbing sets.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
LASER trains a reinforcement learning policy inside a latent dynamics model to choose sensor placements that improve reconstruction of continuum fields under sparsity.
citing papers explorer
-
Data-driven discovery of polynomial ODEs with provably bounded solutions
SILAS jointly optimizes polynomial ODE vector fields and polynomial Lyapunov functions from data to produce models with provably bounded trajectories via compact absorbing sets.
-
LASER: Learning Active Sensing for Continuum Field Reconstruction
LASER trains a reinforcement learning policy inside a latent dynamics model to choose sensor placements that improve reconstruction of continuum fields under sparsity.