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 ...
A CBBL research team led by Professor Balachandran Manavalan from the Department of Integrative Biotechnology at Sungkyunkwan ...
For nearly a decade, the idea that “the body keeps the score” has shaped public and clinical understanding of trauma (van der Kolk, 2014). It is an enticing metaphor—implying that experience is ...
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 ...
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 ...
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 ...
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 ...
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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