Chaotic time series prediction based on physics-informed neural operator
This paper investigates the prediction of chaotic time series using physics-informed neural
operator (PINO) with different driven methods, such as data-driven method, physics-driven …
operator (PINO) with different driven methods, such as data-driven method, physics-driven …
[HTML][HTML] Reconstruction, forecasting, and stability of chaotic dynamics from partial data
The forecasting and computation of the stability of chaotic systems from partial observations
are tasks for which traditional equation-based methods may not be suitable. In this …
are tasks for which traditional equation-based methods may not be suitable. In this …
[HTML][HTML] RF-PINNs: Reactive Flow Physics-Informed Neural Networks for Field Reconstruction of Laminar and Turbulent Flames using Sparse Data
V Yadav, M Casel, A Ghani - Journal of Computational Physics, 2024 - Elsevier
Abstract Physics-Informed Neural Networks (PINNs) have emerged as a promising tool to
model flow fields by embedding physical laws into neural networks and thereby reducing the …
model flow fields by embedding physical laws into neural networks and thereby reducing the …
Van der pol-informed neural networks for multi-step-ahead forecasting of extreme climatic events
Deep learning has produced excellent results in several applied domains including
computer vision, natural language processing, speech recognition, etc. Physics-informed …
computer vision, natural language processing, speech recognition, etc. Physics-informed …
Pump Scheduling Optimization in Urban Water Supply Stations: A Physics‐Informed Multiagent Deep Reinforcement Learning Approach
H Ma, X Wang, D Wang - International Journal of Energy …, 2024 - Wiley Online Library
In the urban water supply system, a significant proportion of energy consumption is
attributed to the water supply pumping station (WSPS). The conventional manual scheduling …
attributed to the water supply pumping station (WSPS). The conventional manual scheduling …