23 citations · 23 across the 6 of their papers we have counts for
7 papers
Petrov-Galerkin operator inference with application to stability-encouraging identification
Johannes Rettberg, Jonas Nicodemus, Harsh Sharma +3
Data-driven model order reduction methods such as operator inference enable the efficient construction of reduced-order models directly from high-dimensional time-domain data. Stan…
Dynamic Shape Control of Soft Robots Enabled by Data-Driven Model Reduction
Iman Adibnazari, Harsh Sharma, Myungsun Park +3
Soft robots have shown immense promise in settings where they can leverage dynamic control of their entire bodies. However, effective dynamic shape control requires a controller th…
Structure-preserving Lift & Learn: Scientific machine learning for nonlinear conservative partial differential equations
Harsh Sharma, Juan Diego Draxl Giannoni, Boris Kramer
This work presents structure-preserving Lift & Learn, a scientific machine learning method that employs lifting variable transformations to learn structure-preserving reduced-order…
Nonlinear energy-preserving model reduction with lifting transformations that quadratize the energy
Harsh Sharma, Juan Diego Draxl Giannoni, Boris Kramer
Existing model reduction techniques for high-dimensional models of conservative partial differential equations (PDEs) encounter computational bottlenecks when dealing with systems…
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…