受强制性开放获取政策约束的文章 - Nicola Rares Franco了解详情
可在其他位置公开访问的文章：7 篇
A deep learning approach validates genetic risk factors for late toxicity after prostate cancer radiotherapy in a REQUITE multi-national cohort
MC Massi, F Gasperoni, F Ieva, AM Paganoni, P Zunino, A Manzoni, ...
Frontiers in oncology 10, 541281, 2020
强制性开放获取政策： US National Institutes of Health, Cancer Research UK, UK Medical Research …
Approximation bounds for convolutional neural networks in operator learning
NR Franco, S Fresca, A Manzoni, P Zunino
Neural Networks 161, 129-141, 2023
强制性开放获取政策： Fondazione Cariplo
Development of a method for generating SNP interaction-aware polygenic risk scores for radiotherapy toxicity
NR Franco, MC Massi, F Ieva, A Manzoni, AM Paganoni, P Zunino, ...
Radiotherapy and Oncology 159, 241-248, 2021
强制性开放获取政策： US National Institutes of Health, National Institute of Health and Medical …
Mesh-Informed Neural Networks for Operator Learning in Finite Element Spaces
NR Franco, A Manzoni, P Zunino
Journal of Scientific Computing 97 (35), 2023
强制性开放获取政策： European Commission
Error estimates for POD-DL-ROMs: a deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper orthogonal decomposition
S Brivio, S Fresca, NR Franco, A Manzoni
Advances in Computational Mathematics 50 (3), 33, 2024
强制性开放获取政策： UK Engineering and Physical Sciences Research Council
Learning high-order interactions for polygenic risk prediction
MC Massi, NR Franco, A Manzoni, AM Paganoni, HA Park, M Hoffmeister, ...
Plos one 18 (2), e0281618, 2023
强制性开放获取政策： German Research Foundation, Federal Ministry of Education and Research, Germany
On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields
NR Franco, D Fraulin, A Manzoni, P Zunino
Advances in Computational Mathematics 50 (5), 96, 2024
强制性开放获取政策： European Commission
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