In Chin.Phys.B 34114704(2025),Eq.(7)and the associated unit notation were incorrect.The correct ones are present here.Since Eq.(7)is an in-built expression in the simulation package,the correction is purely typographi...In Chin.Phys.B 34114704(2025),Eq.(7)and the associated unit notation were incorrect.The correct ones are present here.Since Eq.(7)is an in-built expression in the simulation package,the correction is purely typographical and does not affect the simulation procedure,numerical results,or the conclusions.展开更多
Water molecules can form hydrogen bonds.At the solid surfaces,the preferential alignment of water molecules due to the heterogeneous atomic distributions can induce ordered hydrogen bond networks of water molecules wi...Water molecules can form hydrogen bonds.At the solid surfaces,the preferential alignment of water molecules due to the heterogeneous atomic distributions can induce ordered hydrogen bond networks of water molecules with spatially heterogeneous patterns and slower dynamics compared to bulk water.Both the confinement and the surface atomic structures can induce the water phase transitions at low dimensional spaces.Here,we review how the phase transitions of interfacial water affect the surface physical behaviors,such as wetting,ice nucleation and the terahertz-wave-water interactions,from solid materials to the biological surfaces.These works help extend our knowledge of the physics properties of the interfacial water,particularly the multi-phase behaviors in materials and biology sciences.展开更多
An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximatio...An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.展开更多
文摘In Chin.Phys.B 34114704(2025),Eq.(7)and the associated unit notation were incorrect.The correct ones are present here.Since Eq.(7)is an in-built expression in the simulation package,the correction is purely typographical and does not affect the simulation procedure,numerical results,or the conclusions.
基金supported by the National Natural Science Foundation of China(Grant Nos.22576126,12074394,12022508).
文摘Water molecules can form hydrogen bonds.At the solid surfaces,the preferential alignment of water molecules due to the heterogeneous atomic distributions can induce ordered hydrogen bond networks of water molecules with spatially heterogeneous patterns and slower dynamics compared to bulk water.Both the confinement and the surface atomic structures can induce the water phase transitions at low dimensional spaces.Here,we review how the phase transitions of interfacial water affect the surface physical behaviors,such as wetting,ice nucleation and the terahertz-wave-water interactions,from solid materials to the biological surfaces.These works help extend our knowledge of the physics properties of the interfacial water,particularly the multi-phase behaviors in materials and biology sciences.
基金supported by the National Natural Science Foundation of China(Nos.12422207 and 12372199).
文摘An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.
