For AI models, there are currently no safety rules that govern the path of actions, which can leads to unanticipated consequences.
AI-enabled research tools can accelerate health research, but their data-science roots may clash with epidemiological ...
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 company said on Tuesday that it was holding back on releasing the new technology but was working with 40 companies to explore how it could prevent cyberattacks. By Kevin Roose Reporting from San ...
It’s far from news to any business leader that our current rate of cyberattacks has become a serious problem. In 2024, 72% of organizations reported an increase in cyber risks, driven by the growing ...
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 ...
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