91 citations · 107 across the 7 of their papers we have counts for
9 papers
Touch2Trace: Tactile-Driven Imitation Learning for Dexterous Cable Tracing
Matteo Grimaldi, David Klee, Ziling Chen +5
Dexterous manipulation of deformable objects demands continuous fingertip-level regulation of pressure, friction, and incipient slip. We study one of the most challenging cases: de…
SlipSense: Multimodal Tactile Learning for Low-Latency and Generalized Slip Detection
Tong Jian, Aditya Thurvas Senthil Kumar, Xinyi Li +7
Slip detection is fundamental to dexterous manipulation, yet existing systems often lack precise characterization of detection latency and cross-platform generalization. We present…
Reinforcement learning-based estimation for partial differential equations
Saviz Mowlavi, Mouhacine Benosman
In systems governed by nonlinear partial differential equations such as fluid flows, the design of state estimators such as Kalman filters relies on a reduced-order model (ROM) tha…
Data-driven control of COVID-19 in buildings: a reinforcement-learning approach
Ashkan Haji Hosseinloo, Saleh Nabi, Anette Hosoi +1
In addition to its public health crisis, COVID-19 pandemic has led to the shutdown and closure of workplaces with an estimated total cost of more than $16 trillion. Given the long…
Physics-Informed Koopman Network
Yuying Liu, Aleksei Sholokhov, Hassan Mansour +1
Koopman operator theory is receiving increased attention due to its promise to linearize nonlinear dynamics. Neural networks that are developed to represent Koopman operators have…
Optimal control of PDEs using physics-informed neural networks
Saviz Mowlavi, Saleh Nabi
Physics-informed neural networks (PINNs) have recently become a popular method for solving forward and inverse problems governed by partial differential equations (PDEs). By incorp…
Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control
Yangchen Pan, Amir-massoud Farahmand, Martha White +3
Recent work has shown that reinforcement learning (RL) is a promising approach to control dynamical systems described by partial differential equations (PDE). This paper shows how…
Reduced-order modeling of fully turbulent buoyancy-driven flows using the Green's function method
M. A. Khodkar, Pedram Hassanzadeh, Saleh Nabi +1
A One-Dimensional (1D) Reduced-Order Model (ROM) has been developed for a 3D Rayleigh-Bénard convection system in the turbulent regime with Rayleigh number . The…
Sparse sensing and DMD based identification of flow regimes and bifurcations in complex flows
Boris Kramer, Piyush Grover, Petros Boufounos +2
We present a sparse sensing framework based on Dynamic Mode Decomposition (DMD) to identify flow regimes and bifurcations in large-scale thermo-fluid systems. Motivated by real-tim…