Physics-informed geometric deep learning for inference tasks in power systems

Jongh, Steven de; Gielnik, Frederik; Mueller, Felicitas; Schmit, Loris; Suriyah, Michael

doi:10.1016/j.epsr.2022.108362

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…[46][47][48] For example, physicsinformed AI models were developed for photovoltaic and solar energy, [49][50][51] wind energy, [52,53] power flow [54,55] and power system management. [56,57] Figure 5 illustrates the principles and procedures of physics-informed AI inverse design for TENG systems, encompassing theoretical analysis, the selection of conductive and dielectric materials, and the design of the contact interface. Table 3 compares the existing physics-informed AI models and AI inverse design models with other AI models in terms of the principle and mechanism, characteristics, application scenario, and applicability in TENGs.…”

Section: Inverse Design Of Tengs By Physics-informed Aimentioning

confidence: 99%

Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

Jiao,

Wang,

Alavi

2023

Advanced Materials

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Triboelectric nanogenerators offer an environmentally friendly approach to harvesting energy from mechanical excitations. This capability has made them widely sought‐after as an efficient, renewable, and sustainable energy source, with the potential to decrease reliance on traditional fossil fuels. However, developing triboelectric nanogenerators with specific output remains a challenge mainly due to the uncertainties associated with their complex designs for real‐life applications. Artificial intelligence‐enabled inverse design is a powerful tool to realize performance‐oriented triboelectric nanogenerators. This is an emerging scientific direction that can address the concerns about the design and optimization of triboelectric nanogenerators leading to a next generation nanogenerator systems. This perspective paper aims at reviewing the principal analysis of triboelectricity, summarizing the current challenges of designing and optimizing triboelectric nanogenerators, and highlighting the physics‐informed inverse design strategies to develop triboelectric nanogenerators. Strategic inverse design is particularly discussed in the contexts of expanding the four‐mode analytical models by physics‐informed artificial intelligence, discovering new conductive and dielectric materials, and optimizing contact interfaces. Various potential development levels of artificial intelligence‐enhanced triboelectric nanogenerators are delineated. Finally, the potential of physics‐informed artificial intelligence inverse design to propel triboelectric nanogenerators from prototypes to multifunctional intelligent systems for real‐life applications is discussed.

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