Lagrangian operator inference enhanced with structure-preserving machine learning for nonintrusive model reduction of mechanical systems

DOI10.1016/J.CMA.2024.116865MaRDI QIDQ6494027

Boris Kramer, Michael D. Todd, Harsh Sharma, David A. Najera-Flores

Publication date: 29 April 2024

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

zbMATH Keywords

mechanical systemsLagrangian mechanicsreduced-order modelingdata-driven methodsscientific machine learningnonintrusive model reduction

Mathematics Subject Classification ID

Mechanics of deformable solids (74-XX) Systems theory; control (93-XX)

Cites Work

• Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds

This page was built for publication: Lagrangian operator inference enhanced with structure-preserving machine learning for nonintrusive model reduction of mechanical systems