Search Results for author: Piyush Pandita

Found 12 papers, 2 papers with code

Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field

no code implementations15 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.

Uncertainty Quantification

Reinforcement Learning based Sequential Batch-sampling for Bayesian Optimal Experimental Design

no code implementations21 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.

Experimental Design Policy Gradient Methods +2

Data-based Discovery of Governing Equations

no code implementations5 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.

Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems

no code implementations14 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.

Bayesian Inference Uncertainty Quantification

A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian Processes

2 code implementations8 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.

Dimensionality Reduction Gaussian Processes

Advances in Bayesian Probabilistic Modeling for Industrial Applications

no code implementations26 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.

Physical Intuition

Learning Arbitrary Quantities of Interest from Expensive Black-Box Functions through Bayesian Sequential Optimal Design

no code implementations16 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.

Towards Scalable Gaussian Process Modeling

no code implementations25 Jul 2019 Piyush Pandita, Jesper Kristensen, Liping Wang

Accurately estimating these hyperparameters is a key ingredient in developing a reliable and generalizable surrogate model.

Bayesian Optimal Design of Experiments For Inferring The Statistical Expectation Of A Black-Box Function

1 code implementation26 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.

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