Presentations

"Lagrangian operator inference enhanced with structure-preserving machine learning for nonintrusive model reduction of mechanical systems" at Model Reduction and Surrogate Modeling 2024 (MORe24) Conference, September 2024.

"Data-driven model reduction of soft robots via Lagrangian operator inference" at International Symposium on Mathematical Theory of Networks and Systems, August 2024.

"Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds" at World Congress on Computational Mechanics, July 2024.

"Preserving Lagrangian structure in data-driven reduced-order modeling of large-scale dynamical systems'' at Virginia Tech Workshop on Nonlinear Model Reduction for Control, May 2023.

"Physics-preserving learning of reduced-order models for large-scale dynamical systems'' at Southern California Applied Mathematics Symposium, April 2023.

"Preserving Lagrangian structure in data-driven reduced-order modeling of large-scale mechanical Systems'' at SIAM Conference on Computational Science and Engineering, March 2023.

"Bayesian identification of nonseparable Hamiltonian systems using stochastic dynamic models'' at IEEE Conference on Decision and Control, December 2022.

"Preserving Lagrangian structure in data-driven reduced-order modeling of large-scale mechanical Systems'' at SIAM Conference on Mathematics of Data Science, September 2022.

"Lagrangian operator inference for data-driven reduced-order modeling of large-scale mechanical Systems'' at 39th Southern California Control Workshop, April 2022.

"Hamiltonian operator inference: Physics-preserving learning of reduced-order models for Hamiltonian systems'' at IACM Conference on Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology, September 2021.

"Symplectic accelerated optimization on SO(3) with Lie group variational integrators'' at IEEE American Control Conference, July 2020.

"Energy-preserving, adaptive time-step Lie group variational integrators for rigid body motion in SE(3)'' at IEEE Conference on Decision and Control, December 2019.

"Energy-preserving, adaptive time-step Lie group variational integrators for the attitude dynamics of a rigid body'' at IEEE American Control Conference, July 2019.

"Hermite-based variational and Galerkin integrators for forced Lagrangian systems" at World Congress on Computational Mechanics, July 2018.

H. Sharma, I. Adibnazari, J. Cervera-Torralba, M.T. Tolley, B. Kramer, "Data-driven Model Order Reduction for Soft Robots via Lagrangian Operator Inference", IEEE RoboSoft Workshop, 2024. [Download]

H. Sharma, N. Galioto, A. Gorodetsky, B. Kramer, "Bayesian Identification of Nonseparable Hamiltonian Systems Using Stochastic Dynamic Models", USACM Thematic Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling (UQ-MLIP), 2022. [Download]