26 citations · 67 across the 15 of their papers we have counts for
15 papers
Heterogenous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Process
Yigitcan Comlek, Sandipp Krishnan Ravi, Piyush Pandita +3
Artificial intelligence and machine learning frameworks have served as computationally efficient mapping between inputs and outputs for engineering problems. These mappings have en…
Interpretable Multi-Source Data Fusion Through Latent Variable Gaussian Process
Sandipp Krishnan Ravi, Yigitcan Comlek, Arjun Pathak +9
With the advent of artificial intelligence and machine learning, various domains of science and engineering communities have leveraged data-driven surrogates to model complex syste…
Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field
Lele Luan, Nesar Ramachandra, Sandipp Krishnan Ravi +6
Modern computational methods, involving highly sophisticated mathematical formulations, enable several tasks like modeling complex physical phenomenon, predicting key properties an…
Reinforcement Learning based Sequential Batch-sampling for Bayesian Optimal Experimental Design
Yonatan Ashenafi, Piyush Pandita, Sayan Ghosh
Engineering problems that are modeled using sophisticated mathematical methods or are characterized by expensive-to-conduct tests or experiments, are encumbered with limited budget…
Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning
Sayan Ghosh, Govinda A. Padmanabha, Cheng Peng +6
One of the critical components in Industrial Gas Turbines (IGT) is the turbine blade. Design of turbine blades needs to consider multiple aspects like aerodynamic efficiency, durab…
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…
Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes
Panagiotis Tsilifis, Piyush Pandita, Sayan Ghosh +3
We present a Bayesian approach to identify optimal transformations that map model input points to low dimensional latent variables. The "projection" mapping consists of an orthonor…
A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian Processes
Raphael Gautier, Piyush Pandita, Sayan Ghosh +1
Modern day engineering problems are ubiquitously characterized by sophisticated computer codes that map parameters or inputs to an underlying physical process. In other situations,…
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…
Learning Arbitrary Quantities of Interest from Expensive Black-Box Functions through Bayesian Sequential Optimal Design
Piyush Pandita, Nimish Awalgaonkar, Ilias Bilionis +1
Estimating arbitrary quantities of interest (QoIs) that are non-linear operators of complex, expensive-to-evaluate, black-box functions is a challenging problem due to missing doma…
Towards Scalable Gaussian Process Modeling
Piyush Pandita, Jesper Kristensen, Liping Wang
Numerous engineering problems of interest to the industry are often characterized by expensive black-box objective experiments or computer simulations. Obtaining insight into the p…
Bayesian Optimal Design of Experiments For Inferring The Statistical Expectation Of A Black-Box Function
Piyush Pandita, Ilias Bilionis, Jitesh Panchal
Bayesian optimal design of experiments (BODE) has been successful in acquiring information about a quantity of interest (QoI) which depends on a black-box function. BODE is charact…
Stochastic Multi-objective Optimization on a Budget: Application to multi-pass wire drawing with quantified uncertainties
Piyush Pandita, Ilias Bilionis, Jitesh Panchal +3
Design optimization of engineering systems with multiple competing objectives is a painstakingly tedious process especially when the objective functions are expensive-to-evaluate c…
Extending Expected Improvement for High-dimensional Stochastic Optimization of Expensive Black-Box Functions
Piyush Pandita, Ilias Bilionis, Jitesh Panchal
Design optimization under uncertainty is notoriously difficult when the objective function is expensive to evaluate. State-of-the-art techniques, e.g, stochastic optimization or sa…