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Journal Articles

Inverse Aerodynamic Design of Gas Turbine Blades Using Probabilistic Machine Learning

Sayan Ghosh, Govinda Anantha Padmanabha, Cheng Peng, Valeria Andreoli, Steven Atkinson, Piyush Pandita, Thomas Vandeputte, Nicholas Zabaras, Liping Wang

J. Mech. Des. February 2022, 144(2): 021706.

Paper No: MD-21-1275

DOI: https://doi.org/10.1115/1.4052301

Published Online: September 21, 2021

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Topics: Design, Modeling, Machine learning, Blades, Computational fluid dynamics

Proceedings Papers

A Probabilistic Machine Learning Framework for Explicit Inverse Design of Industrial Gas Turbine Blades

Sayan Ghosh, Valeria Andreoli, Govinda A. Padmanabha, Cheng Peng, Steven Atkinson, Piyush Pandita, Thomas Vandeputte, Nicholas Zabaras, Liping Wang

Proc. ASME. GT2021, Volume 9B: Structures and Dynamics — Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V09BT27A003, June 7–11, 2021

Paper No: GT2021-58842

DOI: https://doi.org/10.1115/GT2021-58842

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Topics: Design, Industrial gases, Machine learning, Turbine blades

Journal Articles

Application of Deep Transfer Learning and Uncertainty Quantification for Process Identification in Powder Bed Fusion

Piyush Pandita, Sayan Ghosh, Vipul K. Gupta, Andrey Meshkov, Liping Wang

ASME J. Risk Uncertainty Part B. March 2022, 8(1): 011106.

Paper No: RISK-21-1010

DOI: https://doi.org/10.1115/1.4051748

Published Online: August 9, 2021

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Topics: Additive manufacturing, Design, Modeling, Simulation, Uncertainty quantification, Lasers, Computers, Physics

Journal Articles

Scalable Fully Bayesian Gaussian Process Modeling and Calibration With Adaptive Sequential Monte Carlo for Industrial Applications

Piyush Pandita, Panagiotis Tsilifis, Sayan Ghosh, Liping Wang

J. Mech. Des. July 2021, 143(7): 074502.

Paper No: MD-20-1623

DOI: https://doi.org/10.1115/1.4050246

Published Online: March 24, 2021

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Topics: Calibration, Modeling, Particle filtering (numerical methods), Algorithms, Chain, Particulate matter

Journal Articles

Advances in Bayesian Probabilistic Modeling for Industrial Applications

Sayan Ghosh, Piyush Pandita, Steven Atkinson, Waad Subber, Yiming Zhang, Natarajan Chennimalai Kumar, Suryarghya Chakrabarti, Liping Wang

ASME J. Risk Uncertainty Part B. September 2020, 6(3): 030904.

Paper No: RISK-19-1074

DOI: https://doi.org/10.1115/1.4046747

Published Online: May 12, 2020

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Topics: Design, Modeling, Sensitivity analysis, Transients (Dynamics), Uncertainty, Calibration

Proceedings Papers

Towards Scalable Gaussian Process Modeling

Piyush Pandita, Jesper Kristensen, Liping Wang

Proc. ASME. IDETC-CIE2019, Volume 2B: 45th Design Automation Conference, V02BT03A032, August 18–21, 2019

Paper No: DETC2019-97010

DOI: https://doi.org/10.1115/DETC2019-97010

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Topics: Modeling, Particle filtering (numerical methods)

Journal Articles

Bayesian Optimal Design of Experiments for Inferring the Statistical Expectation of Expensive Black-Box Functions

Piyush Pandita, Ilias Bilionis, Jitesh Panchal

J. Mech. Des. October 2019, 141(10): 101404.

Paper No: MD-18-1592

DOI: https://doi.org/10.1115/1.4043930

Published Online: July 10, 2019

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Topics: Design, Experimental design, Uncertainty, Steel, Algorithms, Wire, Optimization

Proceedings Papers

Deriving Information Acquisition Criteria for Sequentially Inferring the Expected Value of a Black-Box Function

Piyush Pandita, Ilias Bilionis, Jitesh Panchal

Proc. ASME. IDETC-CIE2018, Volume 2B: 44th Design Automation Conference, V02BT03A057, August 26–29, 2018

Paper No: DETC2018-85893

DOI: https://doi.org/10.1115/DETC2018-85893

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Topics: Approximation, Computer simulation, Errors, Experimental design, Manufacturing, Steel, Wire

Proceedings Papers

Extending Expected Improvement for High-Dimensional Stochastic Optimization of Expensive Black-Box Functions

Piyush Pandita, Ilias Bilionis, Jitesh Panchal

Proc. ASME. IDETC-CIE2016, Volume 2B: 42nd Design Automation Conference, V02BT03A060, August 21–24, 2016

Paper No: DETC2016-60527

DOI: https://doi.org/10.1115/DETC2016-60527

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Topics: Optimization, Uncertainty, Approximation, Design, Chain, Engineering simulation, Filters, Measurement uncertainty, Particulate matter, Permeability

Journal Articles

Extending Expected Improvement for High-Dimensional Stochastic Optimization of Expensive Black-Box Functions

Piyush Pandita, Ilias Bilionis, Jitesh Panchal

J. Mech. Des. November 2016, 138(11): 111412.

Paper No: MD-16-1215

DOI: https://doi.org/10.1115/1.4034104

Published Online: September 12, 2016

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Topics: Design, Optimization, Uncertainty, Simulation, Noise (Sound), Approximation

Journal citation

NARROW

Inverse Aerodynamic Design of Gas Turbine Blades Using Probabilistic Machine Learning

A Probabilistic Machine Learning Framework for Explicit Inverse Design of Industrial Gas Turbine Blades

Application of Deep Transfer Learning and Uncertainty Quantification for Process Identification in Powder Bed Fusion

Scalable Fully Bayesian Gaussian Process Modeling and Calibration With Adaptive Sequential Monte Carlo for Industrial Applications

Advances in Bayesian Probabilistic Modeling for Industrial Applications

Towards Scalable Gaussian Process Modeling

Bayesian Optimal Design of Experiments for Inferring the Statistical Expectation of Expensive Black-Box Functions

Deriving Information Acquisition Criteria for Sequentially Inferring the Expected Value of a Black-Box Function

Extending Expected Improvement for High-Dimensional Stochastic Optimization of Expensive Black-Box Functions

Extending Expected Improvement for High-Dimensional Stochastic Optimization of Expensive Black-Box Functions

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