AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
2 papers indexed here
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
Gradient-based photonic design needs full-wave derivatives with respect to millions of parameters, but on a single GPU workstation the time-domain adjoint is limited by device memory and by the incompatibility of fused update kernels with automatic differentiation. Here, we present TorchFDTD, an open-source finite-diff...
Full-wave electromagnetic simulations provide accurate field distributions for nanophotonic structures, but their high computational cost limits their direct use in large-scale inverse design. Here, we introduce a deep-learning-assisted inverse-design framework in which a neural network is trained with finite-differenc...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.