REVIEW 3 cited by
Efficiently Parameterized Neural Metriplectic Systems
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Metriplectic systems are learned from data in a way that scales quadratically in both the size of the state and the rank of the metriplectic data. Besides being provably energy conserving and entropy stable, the proposed approach comes with approximation results demonstrating its ability to accurately learn metriplectic dynamics from data as well as an error estimate indicating its potential for generalization to unseen timescales when approximation error is low. Examples are provided which illustrate performance in the presence of both full state information as well as when entropic variables are unknown, confirming that the proposed approach exhibits superior accuracy and scalability without compromising on model expressivity.
Forward citations
Cited by 3 Pith papers
-
CaLiSym: Learning Symplectic Dynamics of Real-World Systems through Structured Canonical Lifts
Lifting non-conservative, actuated, and contact-constrained robot dynamics into an exactly symplectic phase-space map yields state-of-the-art out-of-distribution autoregressive rollout error at low parameter and FLOP cost.
-
Structure-Preserving Digital Twins via Conditional Neural Whitney Forms
A transformer-based architecture learns a structure-preserving reduced finite element model, with conservation laws held exactly by the finite element exterior calculus construction, for data-calibrated real-time digi...
-
Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics
A discrete forced Lagrangian neural network learns conservative and dissipative dynamics from position data alone and produces structure-preserving rollouts.
Discussion (0). Sign in to comment.