A reference-decoupled reformulation makes direct data-driven LQT equivalent to certainty-equivalence solutions and supports convergent offline and online DeePO algorithms.
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5 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
VHYDRO is a support-safe variational hybrid filter that jointly recovers continuous latent states, discrete contact modes, and sparse port-Hamiltonian laws per regime while preventing loss of feasible transitions.
A complex-weight extension to the Equation Learner enables stable recovery of symbolic expressions containing real-domain poles and unconstrained use of singular operators such as division and logarithm.
A cost-aware space-filling input design method using Gaussian processes for nonlinear system identification that reduces experimental cost while preserving model performance.
SAGE-Fit improves symbolic regression evaluation by exploiting structural and semantic priors to enhance parameter optimization in non-convex inner-loop fitting.
citing papers explorer
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Direct Data-Driven Linear Quadratic Tracking via Policy Optimization
A reference-decoupled reformulation makes direct data-driven LQT equivalent to certainty-equivalence solutions and supports convergent offline and online DeePO algorithms.
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Support-Safe Variational Hybrid Filtering for Contact-Mode and Sparse-Law Recovery
VHYDRO is a support-safe variational hybrid filter that jointly recovers continuous latent states, discrete contact modes, and sparse port-Hamiltonian laws per regime while preventing loss of feasible transitions.
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Complex Equation Learner: Rational Symbolic Regression with Gradient Descent in Complex Domain
A complex-weight extension to the Equation Learner enables stable recovery of symbolic expressions containing real-domain poles and unconstrained use of singular operators such as division and logarithm.
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Least Costly Space-Filling Experiment Design for the Identification of a Nonlinear System
A cost-aware space-filling input design method using Gaussian processes for nonlinear system identification that reduces experimental cost while preserving model performance.
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When Good Equations Get Bad Scores: Improving Symbolic Regression Through Better Parameter Optimization
SAGE-Fit improves symbolic regression evaluation by exploiting structural and semantic priors to enhance parameter optimization in non-convex inner-loop fitting.