University Of Toronto Department Of Physics In the past decade, a new research frontier emerges at the interface between physics and renewable energy, termed as the inelastic thermoelectric effects where inelastic transport processes play. professor, zhejiang normal university, china cited by 1,685 quantum thermodynamics quantum optics quantum transport.
Colloquium Chen Wang Univ Of Massachusetts Towards Next Generation Zhejiang normal university: jinhua, zhejiang, cn 2018 01 01 to present | associate professor (physics) employment show more detail. Loop is the open research network that increases the discoverability and impact of researchers and their work. loop enables you to stay up to date with the latest discoveries and news, connect with researchers and form new collaborations. We demonstrate that the inelastic quantum heat engine exhibits dramatic advantage of thermodynamic performance compared to their elastic counterpart. moreover, it is found that inelastic currents, output work, and the efficiency can be enhanced by quantum coherence. Manipulating quantum thermal transport relies on uncovering the principle working cycles of quantum devices. here we introduce the cycle flux ranking of network analysis to nonequilibrium thermal devices characterized as a quantum transition network.
Jinhua Wang Associate Professor Phd Xi An Jiaotong University Xi We demonstrate that the inelastic quantum heat engine exhibits dramatic advantage of thermodynamic performance compared to their elastic counterpart. moreover, it is found that inelastic currents, output work, and the efficiency can be enhanced by quantum coherence. Manipulating quantum thermal transport relies on uncovering the principle working cycles of quantum devices. here we introduce the cycle flux ranking of network analysis to nonequilibrium thermal devices characterized as a quantum transition network. Listed: chenglin wang (xihua university, school of science) jian zhang (university of electronic science and technology of china, school of mathematical sciences) shihui zhu (sichuan normal university, school of mathematical sciences). A physics informed neural network is developed to infer the unknown heat flux in a 1d inverse heat conduction problem. this is achieved by training the neural network by physics constraints including the governing equation, boundary and initial conditions, and sampled temperature data. A neural network based 3 d dynamic electron density model is developed in the inner magnetosphere the den3d model successfully reproduced the quiet time structure, plasmaspheric erosion, and refill. Abstract background: in computational biology, embedding known physical laws into deep learning models to construct "physics informed neural networks" (pinns) is a mainstream paradigm for enhancing model interpretability and extrapolation capability. however, in complex multi physics coupling problems, there is a risk of competitive imbalance between the physical term and the flexible.
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