no code implementations • 6 Feb 2024 • Sandipp Krishnan Ravi, Yigitcan Comlek, Wei Chen, Arjun Pathak, Vipul Gupta, Rajnikant Umretiya, Andrew Hoffman, Ghanshyam Pilania, Piyush Pandita, Sayan Ghosh, Nathaniel Mckeever, Liping Wang
Towards resolving this issue, a multi-source data fusion framework based on Latent Variable Gaussian Process (LVGP) is proposed.
no code implementations • 15 Mar 2023 • Lele Luan, Nesar Ramachandra, Sandipp Krishnan Ravi, Anindya Bhaduri, Piyush Pandita, Prasanna Balaprakash, Mihai Anitescu, Changjie Sun, Liping Wang
Modern computational methods, involving highly sophisticated mathematical formulations, enable several tasks like modeling complex physical phenomenon, predicting key properties and design optimization.
no code implementations • 21 Dec 2021 • Yonatan Ashenafi, Piyush Pandita, Sayan Ghosh
In such scenarios, one usually resorts to performing experiments in a manner that allows for maximizing one's state-of-knowledge while satisfying the above mentioned practical constraints.
no code implementations • 17 Aug 2021 • Sayan Ghosh, Govinda A. Padmanabha, Cheng Peng, Steven Atkinson, Valeria Andreoli, Piyush Pandita, Thomas Vandeputte, Nicholas Zabaras, Liping Wang
One of the critical components in Industrial Gas Turbines (IGT) is the turbine blade.
no code implementations • 5 Dec 2020 • Waad Subber, Piyush Pandita, Sayan Ghosh, Genghis Khan, Liping Wang, Roger Ghanem
Without a prior definition of the model structure, first a free-form of the equation is discovered, and then calibrated and validated against the available data.
no code implementations • 14 Aug 2020 • Waad Subber, Sayan Ghosh, Piyush Pandita, Yiming Zhang, Liping Wang
The region of interest can be specified based on the localization features of the solution, user interest, and correlation length of the random material properties.
2 code implementations • 8 Aug 2020 • Raphael Gautier, Piyush Pandita, Sayan Ghosh, Dimitri Mavris
The comparison shows that the proposed method improves the active subspace recovery and predictive accuracy, in both the deterministic and probabilistic sense, when only few model observations are available for training, at the cost of increased training time.
no code implementations • 5 Aug 2020 • Panagiotis Tsilifis, Piyush Pandita, Sayan Ghosh, Valeria Andreoli, Thomas Vandeputte, Liping Wang
We present a Bayesian approach to identify optimal transformations that map model input points to low dimensional latent variables.
no code implementations • 26 Mar 2020 • Sayan Ghosh, Piyush Pandita, Steven Atkinson, Waad Subber, Yiming Zhang, Natarajan Chennimalai Kumar, Suryarghya Chakrabarti, Liping Wang
The methodology, called GE's Bayesian Hybrid Modeling (GEBHM), is a probabilistic modeling method, based on the Kennedy and O'Hagan framework, that has been continuously scaled-up and industrialized over several years.
no code implementations • 16 Dec 2019 • Piyush Pandita, Nimish Awalgaonkar, Ilias Bilionis, Jitesh Panchal
We model the underlying information source as a fully-Bayesian, non-stationary Gaussian process (FBNSGP), and derive an approximation of the information gain of a hypothetical experiment about an arbitrary QoI conditional on the hyper-parameters The EIG about the same QoI is estimated by sample averages to integrate over the posterior of the hyper-parameters and the potential experimental outcomes.
no code implementations • 25 Jul 2019 • Piyush Pandita, Jesper Kristensen, Liping Wang
Accurately estimating these hyperparameters is a key ingredient in developing a reliable and generalizable surrogate model.
1 code implementation • 26 Jul 2018 • Piyush Pandita, Ilias Bilionis, Jitesh Panchal
Our hypothesis is that an optimal BODE should be maximizing the expected information gain in the QoI.