With the increasing concern on energy crisis, coordination of multiple sources and low-carbon economic operation integrated system (IES) have drawn more attention in recent years. In IES, accurate effective multi-energy load forecasting becomes a research hotspot, especially using high-performance data mining machine learning algorithms. However, due to huge difference utilization between IES t...

In the initial construction of unit-level virtual models in discrete production system, understanding physical entities and actual operating environment is lacking, need to be iteratively updated resemble more closely. To solve above problems, an updating method for knowledge model driven by data studied. Firstly, behavior can divided into basic functional such as forward kinematics, trajectory...

Abstract In this paper, we propose a sensitivity-free and multi-objective structural design methodology called data-driven topology . It is schemed to obtain high-performance material distributions from initially given in domain. Its basic idea iterate the following processes: (i) selecting dataset of according eliteness, (ii) generating new using deep generative model trained with selected eli...

Minimal dimensional models are desirable for reduced computational costs in simulations as well applications such model-based control. Long-time dynamics of flows often evolve on a low-dimensional manifold M the full state space. We use neural networks to estimate and it two-dimensional Kolmogorov flow chaotic bursting regime. Outcomes include: minimal dimension estimate, good short-time tracki...