We present a fast and accurate data-driven surrogate model for divertor plasma detachment prediction leveraging the latent feature space concept in machine learning research. Our approach involves constructing training two neural networks. An autoencoder that finds proper representation (LSR) of state by compressing multi-modal diagnostic measurements, forward using multi-layer perception (MLP)...

Here the idea of data driven is introduced in model predictive control to establish our proposed data-driven control. Considering one first-order discrete time nonlinear dynamical ...

KRas4B (henceforth called KRas) is a small GTPase that regulates activation of variety signaling pathways, including the Raf/MEK/ERK pathway, leading to cell proliferation, survival, and differentiation. Activation these pathways dependent on nucleotide-bound state KRas: GTP-bound KRas “turns on” while GDP-bound off” signaling. Interestingly, only one three Ras isoforms interacts with Ca2+-boun...

An important problem in nuclear fusion plasmas is the prediction and control of turbulence which drives cross-field transport, thus leading to energy loss from system deteriorating confinement. Turbulence, being a highly nonlinear multiscale process, challenging theoretically describe computationally model. Most advanced computational models fall into one two categories: fluid or gyro-kinetic. ...

Abstract We investigate the use of reduced-order modelling to run discrete element simulations at higher speeds. Taking a data-driven approach, we many offline in advance and train model predict velocity field from mass distribution system control signals. Rapid inference particle velocities replaces intense process computing contact forces updates. In coupled DEM multibody simulation, predicto...