64 citations
- General Electric (United States)US2 papers
- Association for Computing MachineryUS1 paper
- Ben-Gurion University of the NegevIL1 paper
- Bielefeld UniversityDE1 paper
- California Southern UniversityUS1 paper
- Cardiff UniversityGB1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
- General Motors (Poland)PL1 paper
- Google (United States)US1 paper
- Institut Jean Le Rond d'AlembertFR1 paper
- Jilin Medical UniversityCN1 paper
- Jilin UniversityCN1 paper
6 papers · 1 filter
Data-based Discovery of Governing Equations
Waad Subber, Piyush Pandita, Sayan Ghosh +3
Most common mechanistic models are traditionally presented in mathematical forms to explain a given physical phenomenon. Machine learning algorithms, on the other hand, provide a m…
Symbolic Semantic Segmentation and Interpretation of COVID-19 Lung Infections in Chest CT volumes based on Emergent Languages
Aritra Chowdhury, Alberto Santamaria-Pang, James R. Kubricht +2
The coronavirus disease (COVID-19) has resulted in a pandemic crippling the a breadth of services critical to daily life. Segmentation of lung infections in computerized tomography…
Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems
Waad Subber, Sayan Ghosh, Piyush Pandita +2
Industrial dynamical systems often exhibit multi-scale response due to material heterogeneities, operation conditions and complex environmental loadings. In such problems, it is th…
Feature Space Augmentation for Long-Tailed Data
Peng Chu, Xiao Bian, Shaopeng Liu +1
Real-world data often follow a long-tailed distribution as the frequency of each class is typically different. For example, a dataset can have a large number of under-represented c…
Bayesian Hidden Physics Models: Uncertainty Quantification for Discovery of Nonlinear Partial Differential Operators from Data
Steven Atkinson
What do data tell us about physics-and what don't they tell us? There has been a surge of interest in using machine learning models to discover governing physical laws such as diff…
Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting
Carolin M. Pirkl, Pedro A. Gómez, Ilona Lipp +13
Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues. It relies on a pseudo-random acquisition and the match…