Search Results for author:
Found 7 papers, 2 papers with code
Low-cost sensors and circuits for plasma education: characterizing power and illuminance
Additionally, we develop a low-cost illuminance sensor and demonstrate how it can be used to estimate the corresponding plasma power.
Stability-informed Bayesian Optimization for MPC Cost Function Learning
Designing predictive controllers towards optimal closed-loop performance while maintaining safety and stability is challenging.
Neural Schrödinger Bridge with Sinkhorn Losses: Application to Data-driven Minimum Effort Control of Colloidal Self-assembly
We show that the minimum effort control of colloidal self-assembly can be naturally formulated in the order-parameter space as a generalized Schr\"{o}dinger bridge problem -- a class of fixed-horizon stochastic optimal control problems that originated in the works of Erwin Schr\"{o}dinger in the early 1930s.
A Physics-informed Deep Learning Approach for Minimum Effort Stochastic Control of Colloidal Self-Assembly
We propose formulating the finite-horizon stochastic optimal control problem for colloidal self-assembly in the space of probability density functions (PDFs) of the underlying state variables (namely, order parameters).
Stochastic Physics-Informed Neural Ordinary Differential Equations
Stochastic differential equations (SDEs) are used to describe a wide variety of complex stochastic dynamical systems.
PoCET: a Polynomial Chaos Expansion Toolbox for Matlab
We introduce PoCET: a free and open-scource Polynomial Chaos Expansion Toolbox for Matlab, featuring the automatic generation of polynomial chaos expansion (PCE) for linear and nonlinear dynamic systems with time-invariant stochastic parameters or initial conditions, as well as several simulation tools.
Systems and Control Systems and Control
Learning-based Stochastic Model Predictive Control with State-Dependent Uncertainty
The increasing complexity of modern engineering systems can introduce a great deal of uncertainty in our knowledge of system dynamics, which can, in turn, pose a major challenge to safe model-based control.
