7 citations · 24 across the 13 of their papers we have counts for
5 papers · 1 filter
Structure-preserving learning for multi-symplectic PDEs
Süleyman Yıldız, Pawan Goyal, Peter Benner
This paper presents an energy-preserving machine learning method for inferring reduced-order models (ROMs) by exploiting the multi-symplectic form of partial differential equations…
Stability-Certified Learning of Control Systems with Quadratic Nonlinearities
Igor Pontes Duff, Pawan Goyal, Peter Benner
This work primarily focuses on an operator inference methodology aimed at constructing low-dimensional dynamical models based on a priori hypotheses about their structure, often in…
Deep Learning for Structure-Preserving Universal Stable Koopman-Inspired Embeddings for Nonlinear Canonical Hamiltonian Dynamics
Pawan Goyal, Süleyman Yıldız, Peter Benner
Discovering a suitable coordinate transformation for nonlinear systems enables the construction of simpler models, facilitating prediction, control, and optimization for complex no…
Active-Learning-Driven Surrogate Modeling for Efficient Simulation of Parametric Nonlinear Systems
Harshit Kapadia, Lihong Feng, Peter Benner
When repeated evaluations for varying parameter configurations of a high-fidelity physical model are required, surrogate modeling techniques based on model order reduction are desi…
Inference of Continuous Linear Systems from Data with Guaranteed Stability
Pawan Goyal, Igor Pontes Duff, Peter Benner
Machine-learning technologies for learning dynamical systems from data play an important role in engineering design. This research focuses on learning continuous linear models from…