79 citations · 214 across the 17 of their papers we have counts for
17 papers
Roadmap on Deep Learning for Microscopy
Giovanni Volpe, Carolina Wählby, Lei Tian +72
Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasin…
Extraction of the frequency moments of spectral densities from imaginary-time correlation function data
Tobias Dornheim, Damar C. Wicaksono, Juan E. Suarez-Cardona +5
We introduce an exact framework to compute the positive frequency moments of different dynamic properties from imaginary-time quantum Monte Carlo…
Learning Partial Differential Equations by Spectral Approximates of General Sobolev Spaces
Juan-Esteban Suarez Cardona, Phil-Alexander Hofmann, Michael Hecht
We introduce a novel spectral, finite-dimensional approximation of general Sobolev spaces in terms of Chebyshev polynomials. Based on this polynomial surrogate model (PSM), we real…
Multivariate Polynomial Regression of Euclidean Degree Extends the Stability for Fast Approximations of Trefethen Functions
Sachin K. Thekke Veettil, Yuxi Zheng, Uwe Hernandez Acosta +2
We address classic multivariate polynomial regression tasks from a novel perspective resting on the notion of general polynomial -degree, with total, Euclidean, and maximum de…
Global Polynomial Level Sets for Numerical Differential Geometry of Smooth Closed Surfaces
Sachin K. Thekke Veettil, Gentian Zavalani, Uwe Hernandez Acosta +2
We present a computational scheme that derives a global polynomial level set parametrisation for smooth closed surfaces from a regular surface-point set and prove its uniqueness. T…
Replacing Automatic Differentiation by Sobolev Cubatures fastens Physics Informed Neural Nets and strengthens their Approximation Power
Juan Esteban Suarez Cardona, Michael Hecht
We present a novel class of approximations for variational losses, being applicable for the training of physics-informed neural nets (PINNs). The loss formulation reflects classic…