A deep learning inverse‐design framework is established to create versatile reconfigurable terahertz metadevices. By synergizing deep learning with phase‐change materials, this approach enables on‐demand customization of multidimensional electromagnetic responses.
Yisheng Dong +11 more
wiley +1 more source
Influence mechanism of thick hard layer on fracture and energy release characteristics of composite roof. [PDF]
Song XS +5 more
europepmc +1 more source
A Bidirectional Design Method for Through-Glass Vias with Selective Laser Wet Etching Based on the Cross-Modal Learning Method. [PDF]
Meng Y +6 more
europepmc +1 more source
Numerical investigation on the torsional improvement of reinforced concrete beams strengthened with various techniques. [PDF]
Yusuf MA, Zahran MS, Osman A, Nagy NM.
europepmc +1 more source
Far-field multi-beam pattern synthesis for phased array antennas using Lorentz reciprocity theorem. [PDF]
Fan Y, Li L, Arya RK, Dong J, Kong S.
europepmc +1 more source
Dataset of high-speed camera measurements from impact-tested reinforced concrete beams. [PDF]
Peterson V.
europepmc +1 more source
Machine-Learning-Based Probabilistic Model and Design-Oriented Formula of Shear Strength Capacity of UHPC Beams. [PDF]
Yang K, Xu J, Ni X.
europepmc +1 more source
Three-photon holographic microscopy for deep precise optogenetics
Lafirdeen AM +18 more
europepmc +1 more source

