feedforward neural netsgeophysics computingisotope relative abundanceleadmultilayer perceptronsltsup>212<Our study describes many new opportunities for using neural networks in physics. We have mapped types of physics problems to analogous applications in other areas of science and engineering. While many ...
In this context, this work presents a new framework called Physics-Informed Neural Nets-based Control (PINC), which proposes a novel PINN-based architecture that is amenable to control problems and able to simulate for longer-range time horizons that are not fixed beforehand. First, the network...
chemical reactorstime series forecasting/ C3350G Control applications in chemical and oil refining industries C1230D Neural nets C1220 Simulation, modelling ... R Baratti,B Cannas,A Fanni,... - 《Neural Computing & Applications》 被引量: 16发表: 2000年 Hybrid Modeling of Modified Mathematical ...
Jin, X., Cai, S., Li, H., et al.: NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations. J. Comput. Phys. 426, 109951 (2021) Raissi, M., Yazdani, A., Karniadakis, G.E.: Hidden fluid mechanics: learning velocity and pre...
Fig. 1: Physics-informed neural network (PINN). a The deep neural network (DNN) model uses input time series measurements (e.g. bioimpedance, BioZ) to estimate continuous systolic, diastolic, and pulse pressure values. Taylor’s approximation is defined for physiological features extracted from ...
Physics-informed neural networks (PINNs) have enabled significant improvements in modelling physical processes described by partial differential equations (PDEs) and are in principle capable of modeling a large variety of differential equations. PINNs are based on simple architectures, and learn the behavi...
To explore the relation between network structure and function, we studied the computational performance of Hopfield-type attractor neural nets with regula... PN Mcgraw,M Menzinger - 《Physical Review E Statistical Nonlinear & Soft Matter Physics》 被引量: 125发表: 2003年 Computational neural network...
HL-nets: Physics-informed neural networks for hydrodynamic lubrication with cavitation 2023, Tribology International Citation Excerpt : Li et al. [47] devised a PINN scheme to solve the Reynolds equation to predict the gas bearing's flow fields and aerodynamic characteristics. Recently, Rom[48] has...
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itself. PINNs are nowadays used to solve PDEs, fractional equations, integral-differential equations, and stochastic PDEs. Th...
The tutorial is divided into six lectures, that where presented at the Third Graduate Summer Course on Computational Physics (September 3–7, 1990) on Parallel Architectures and Applications, organized by the European Physical Society: (1) Introduction: machine learning and biological computation. (2...