Search Results for author:
Found 9 papers, 2 papers with code
A method for comparing multiple imputation techniques: a case study on the U.S. National COVID Cohort Collaborative
In this paper, we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques.
Inverse-Designed Meta-Optics with Spectral-Spatial Engineered Response to Mimic Color Perception
Meta-optics have rapidly become a major research field within the optics and photonics community, strongly driven by the seemingly limitless opportunities made possible by controlling optical wavefronts through interaction with arrays of sub-wavelength scatterers.
Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport
Deep neural operators can learn operators mapping between infinite-dimensional function spaces via deep neural networks and have become an emerging paradigm of scientific machine learning.
End-to-End Optimization of Metasurfaces for Imaging with Compressed Sensing
We present a framework for the end-to-end optimization of metasurface imaging systems that reconstruct targets using compressed sensing, a technique for solving underdetermined imaging problems when the target object exhibits sparsity (i. e. the object can be described by a small number of non-zero values, but the positions of these values are unknown).
Physics-enhanced deep surrogates for partial differential equations
Many physics and engineering applications demand Partial Differential Equations (PDE) property evaluations that are traditionally computed with resource-intensive high-fidelity numerical solvers.
Physics-informed neural networks with hard constraints for inverse design
We achieve the same objective as conventional PDE-constrained optimization methods based on adjoint methods and numerical PDE solvers, but find that the design obtained from hPINN is often simpler and smoother for problems whose solution is not unique.
Topology Optimization of Surface-enhanced Raman Scattering Substrates
We then show that, by relaxing the fabrication constraints, TopOpt may be used to design SERS substrates with orders of magnitude larger enhancement factor.
Mesoscale and Nanoscale Physics
Modified discrete Laguerre polynomials for efficient computation of exponentially bounded Matsubara sums
We develop a new type of orthogonal polynomial, the modified discrete Laguerre (MDL) polynomials, designed to accelerate the computation of bosonic Matsubara sums in statistical physics.
Numerical Analysis Numerical Analysis Computational Physics
Active learning of deep surrogates for PDEs: Application to metasurface design
Surrogate models for partial-differential equations are widely used in the design of meta-materials to rapidly evaluate the behavior of composable components.
