Given the functional complexities of soft tissues and organs, it is clear that computational simulations are critical in their understanding and for the rational basis for the development of therapies and replacements. A key aspect of such simulation...
We introduce universal solution manifold network (USM-Net), a novel surrogate model, based on artificial neural networks (ANNs), which applies to differential problems whose solution depends on physical and geometrical parameters. We employ a mesh-le...
Non-interferometric quantitative phase imaging based on Transport of Intensity Equation (TIE) has been widely used in bio-medical imaging. However, analytic TIE phase retrieval is prone to low-spatial frequency noise amplification, which is caused by...
Resonance analysis and structural optimization of multi-channel selective fiber couplers currently rely on numerical simulation and manual trial and error, which is very repetitive and time consuming. To realize fast and accurate resonance analysis a...
The brains of some insects can encode and decode polarization information and obtain heading angle information. Referring to the encoding ability of insects, exponential function encoding is designed to improve the stability of the polarized light co...
Recent achievements in the field of electronic skin (e-skin) have provided promising technology for service robots. However, the development of a bionic perception system that exhibits superior performance in terms of safety and interaction quality r...
Recently, deep reinforcement learning (DRL) for metasurface design has received increased attention for its excellent decision-making ability in complex problems. However, time-consuming numerical simulation has hindered the adoption of DRL-based des...
The two-dimensional optical metasurface can realize the free regulation of light waves through the free design of structure, which is highly appreciated by researchers. As there are high requirements for computer hardware, long time for simulation ca...
Machine learning (ML) based interatomic potentials are emerging tools for material simulations, but require a trade-off between accuracy and speed. Here, we show how one can use one ML potential model to train another: we use an accurate, but more co...
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