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Found 5 papers, 1 papers with code
Discrete-time Flatness-based Control of a Gantry Crane
This article addresses the design of a discrete-time flatness-based tracking control for a gantry crane and demonstrates the practical applicability of the approach by measurement results.
Embedded-physics machine learning for coarse-graining and collective variable discovery without data
Rather than separating model learning from the data-generation procedure - the latter relies on simulating atomistic motions governed by force fields - we query the atomistic force field at sample configurations proposed by the predictive coarse-grained model.
On Structural Invariants in the Energy-Based Control of Infinite-Dimensional Port-Hamiltonian Systems with In-Domain Actuation
This contribution deals with energy-based in-domain control of systems governed by partial differential equations with spatial domain up to dimension two.
Optimization and Control
Predictive Collective Variable Discovery with Deep Bayesian Models
In this work, we formulate the discovery of CVs as a Bayesian inference problem and consider the CVs as hidden generators of the full-atomistic trajectory.
Predictive Coarse-Graining
We propose a data-driven, coarse-graining formulation in the context of equilibrium statistical mechanics.
