2 citations · 4 across the 8 of their papers we have counts for
14 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…