23 citations · 23 across the 6 of their papers we have counts for
3 papers · 1 filter
Data-driven Model Reduction for Soft Robots via Lagrangian Operator Inference
Harsh Sharma, Iman Adibnazari, Jacobo Cervera-Torralba +2
Data-driven model reduction methods provide a nonintrusive way of constructing computationally efficient surrogates of high-fidelity models for real-time control of soft robots. Th…
Lagrangian operator inference enhanced with structure-preserving machine learning for nonintrusive model reduction of mechanical systems
Harsh Sharma, David A. Najera-Flores, Michael D. Todd +1
Complex mechanical systems often exhibit strongly nonlinear behavior due to the presence of nonlinearities in the energy dissipation mechanisms, material constitutive relationships…
Bayesian identification of nonseparable Hamiltonians with multiplicative noise using deep learning and reduced-order modeling
Nicholas Galioto, Harsh Sharma, Boris Kramer +1
This paper presents a structure-preserving Bayesian approach for learning nonseparable Hamiltonian systems using stochastic dynamic models allowing for statistically-dependent, vec…