Data-Centric Engineering: integrating simulation, machine learning and statistics. Challenges and Opportunities

7 November 2021

Papers citing "Data-Centric Engineering: integrating simulation, machine learning and statistics. Challenges and Opportunities"

7 / 7 papers shown

Title
Physics-Informed Variational State-Space Gaussian Processes Oliver Hamelijnck Arno Solin Theodoros Damoulas 31 0 0 20 Sep 2024
DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence era David Restrepo Chenwei Wu Constanza Vásquez-Venegas Luis Filipe Nakayama Leo Anthony Celi Diego M. Lopez 27 13 0 18 Apr 2024
A Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulation Yu Duan M. Eaton Michael Bluck 19 0 0 13 Dec 2023
Data Optimization in Deep Learning: A Survey Ou Wu Rujing Yao 35 1 0 25 Oct 2023
Introducing Hybrid Modeling with Time-series-Transformers: A Comparative Study of Series and Parallel Approach in Batch Crystallization Niranjan Sitapure J. Kwon 35 32 0 25 Jul 2023
Efficient hybrid modeling and sorption model discovery for non-linear advection-diffusion-sorption systems: A systematic scientific machine learning approach Vinícius V. Santana É. Costa C. Rebello A. M. Ribeiro Chris Rackauckas Idelfonso B. R. Nogueira 15 8 0 22 Mar 2023
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 287 5,837 0 08 Jul 2016