no code implementations • 13 Jun 2022 • Elena Casiraghi, Rachel Wong, Margaret Hall, Ben Coleman, Marco Notaro, Michael D. Evans, Jena S. Tronieri, Hannah Blau, Bryan Laraway, Tiffany J. Callahan, Lauren E. Chan, Carolyn T. Bramante, John B. Buse, Richard A. Moffitt, Til Sturmer, Steven G. Johnson, Yu Raymond Shao, Justin Reese, Peter N. Robinson, Alberto Paccanaro, Giorgio Valentini, Jared D. Huling, Kenneth Wilkins, :, Tell Bennet, Christopher Chute, Peter DeWitt, Kenneth Gersing, Andrew Girvin, Melissa Haendel, Jeremy Harper, Janos Hajagos, Stephanie Hong, Emily Pfaff, Jane Reusch, Corneliu Antoniescu, Kimberly Robaski
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.
no code implementations • 28 Apr 2022 • Chris Munley, Wenchao Ma, Johannes E. Fröch, Quentin A. A. Tanguy, Elyas Bayati, Karl F. Böhringer, Zin Lin, Raphaël Pestourie, Steven G. Johnson, Arka Majumdar
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.
2 code implementations • 14 Apr 2022 • Lu Lu, Raphael Pestourie, Steven G. Johnson, Giuseppe Romano
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.
no code implementations • 28 Jan 2022 • Gaurav Arya, William F. Li, Charles Roques-Carmes, Marin Soljačić, Steven G. Johnson, Zin Lin
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).
no code implementations • 10 Nov 2021 • Raphaël Pestourie, Youssef Mroueh, Chris Rackauckas, Payel Das, Steven G. Johnson
Many physics and engineering applications demand Partial Differential Equations (PDE) property evaluations that are traditionally computed with resource-intensive high-fidelity numerical solvers.
4 code implementations • 9 Feb 2021 • Lu Lu, Raphael Pestourie, Wenjie Yao, Zhicheng Wang, Francesc Verdugo, Steven G. Johnson
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.
no code implementations • 27 Jan 2021 • Ying Pan, Rasmus E. Christiansen, Jerome Michon, Juejun Hu, Steven G. Johnson
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
no code implementations • 5 Jan 2021 • Guanpeng Xu, Steven G. Johnson
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
no code implementations • 24 Aug 2020 • Raphaël Pestourie, Youssef Mroueh, Thanh V. Nguyen, Payel Das, Steven G. Johnson
Surrogate models for partial-differential equations are widely used in the design of meta-materials to rapidly evaluate the behavior of composable components.