32 citations · 40 across the 5 of their papers we have counts for
5 papers
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…
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…
Advances in Bayesian Probabilistic Modeling for Industrial Applications
Sayan Ghosh, Piyush Pandita, Steven Atkinson +5
Industrial applications frequently pose a notorious challenge for state-of-the-art methods in the contexts of optimization, designing experiments and modeling unknown physical resp…
Data-driven discovery of free-form governing differential equations
Steven Atkinson, Waad Subber, Liping Wang +3
We present a method of discovering governing differential equations from data without the need to specify a priori the terms to appear in the equation. The input to our method is a…
A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty
Sayan Ghosh, Jesper Kristensen, Yiming Zhang +2
Multi-fidelity Gaussian process is a common approach to address the extensive computationally demanding algorithms such as optimization, calibration and uncertainty quantification.…