Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Negrini, Elisa"'
Autor:
Manekar, Raunak, Negrini, Elisa, Pham, Minh, Jacobs, Daniel, Srivastava, Jaideep, Osher, Stanley J., Miao, Jianwei
Phase retrieval (PR) is fundamentally important in scientific imaging and is crucial for nanoscale techniques like coherent diffractive imaging (CDI). Low radiation dose imaging is essential for applications involving radiation-sensitive samples. How
Externí odkaz:
http://arxiv.org/abs/2402.17745
Autor:
Ersin, Pelin, Hayes, Emma, Matthews, Peter, Mohapatra, Paramjyoti, Negrini, Elisa, Schulz, Karl
Neural networks have become a powerful tool as surrogate models to provide numerical solutions for scientific problems with increased computational efficiency. This efficiency can be advantageous for numerically challenging problems where time to sol
Externí odkaz:
http://arxiv.org/abs/2310.12046
We demonstrate that in situ coherent diffractive imaging (CDI), which harnesses the coherent interference between a strong and a weak beam illuminating a static and dynamic structure, can be a very dose-efficient imaging method. At low doses, in situ
Externí odkaz:
http://arxiv.org/abs/2306.11283
We prove that Sobolev spaces on Cartesian and warped products of metric spaces tensorize, only requiring that one of the factors is a doubling space supporting a Poincar'e inequality.
Comment: 23 pages
Comment: 23 pages
Externí odkaz:
http://arxiv.org/abs/2305.05804
Autor:
Negrini, Elisa, Nurbekyan, Levon
In this work, we investigate applications of no-collision transportation maps introduced in [Nurbekyan et. al., 2020] in manifold learning for image data. Recently, there has been a surge in applying transportation-based distances and features for da
Externí odkaz:
http://arxiv.org/abs/2304.00199
We present a new algorithm for learning unknown governing equations from trajectory data, using and ensemble of neural networks. Given samples of solutions $x(t)$ to an unknown dynamical system $\dot{x}(t)=f(t,x(t))$, we approximate the function $f$
Externí odkaz:
http://arxiv.org/abs/2110.08382
We study an optimal transportation approach for recovering parameters in dynamical systems with a single smoothly varying attractor. We assume that the data is not sufficient for estimating time derivatives of state variables but enough to approximat
Externí odkaz:
http://arxiv.org/abs/2104.15138
In this paper we use neural networks to learn governing equations from data. Specifically we reconstruct the right-hand side of a system of ODEs $\dot{x}(t) = f(t, x(t))$ directly from observed uniformly time-sampled data using a neural network. In c
Externí odkaz:
http://arxiv.org/abs/2009.03288
Akademický článek
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Publikováno v:
In Journal of Computational Physics 1 November 2021 444
