32 citations · 71 across the 11 of their papers we have counts for
11 papers
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…
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…
Discovery of Physics and Characterization of Microstructure from Data with Bayesian Hidden Physics Models
Steven Atkinson, Yiming Zhang, Liping Wang
There has been a surge in the interest of using machine learning techniques to assist in the scientific process of formulating knowledge to explain observational data. We demonstra…
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…
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…
Bayesian task embedding for few-shot Bayesian optimization
Steven Atkinson, Sayan Ghosh, Natarajan Chennimalai-Kumar +2
We describe a method for Bayesian optimization by which one may incorporate data from multiple systems whose quantitative interrelationships are unknown a priori. All general (nonr…
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.…
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…