Abstract: This paper presents a novel approach to practical nonlinear model predictive control (PNMPC) using Kolmogorov–Arnold networks (KANs) as prediction models. KANs are based on the ...

Public experiment log using Get Physics Done (GPD) with Codex to explore predictive control of tokamak plasma turbulence and confinement. A physics-based flight simulator for optimizing airbrake ...

The current microgrids are experiencing growing difficulties in voltage stability and operational capacity, particularly with constant power loads (CPLs), leading to negative impedance behavior and ...

Google has quietly reworked Gemini‘s usage limits, splitting the shared pool and boosting the individual caps for the Thinking and Pro models. At launch, both models had the same daily quota, meaning ...

Abstract: Model-free predictive control (MFPC) has become a popular choice for addressing the robustness limitations of model-based predictive control (MBPC), by replacing physical models with ...

do-mpc is a comprehensive open-source toolbox for robust model predictive control (MPC) and moving horizon estimation (MHE). do-mpc enables the efficient formulation and solution of control and ...

To drive growth, companies should transform customer support from reactive to predictive and proactive. Using foresight, ethical data and strategic alignment can turn customer experience into a key ...

A new kind of large language model, developed by researchers at the Allen Institute for AI (Ai2), makes it possible to control how training data is used even after a model has been built.