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Proceedings Papers

Industrial Applications of Transfer Learning Methods for Aircraft Propulsion Systems

Ryan Jacobs, Sandipp Krishnan, Anindya Bhaduri, Lele Luan, Piyush Pandita, Sayan Ghosh, Liping Wang

Proc. ASME. GT2024, Volume 10B: Structures and Dynamics — Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V10BT25A004, June 24–28, 2024

Publisher: American Society of Mechanical Engineers

Paper No: GT2024-125838

https://doi.org/10.1115/GT2024-125838

View Paper titled, Industrial Applications of Transfer Learning Methods for Aircraft Propulsion Systems

Topics: Aircraft propulsion, Design, Maintenance, Propulsion, Blades, Machine learning, Artificial neural networks, Cycles, Deep neural networks, Downtime

Journal Articles

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Anirban Samaddar, Sandipp Krishnan Ravi, Nesar Ramachandra, Lele Luan, Sandeep Madireddy, Anindya Bhaduri, Piyush Pandita, Changjie Sun, Liping Wang

Publisher: ASME

J. Mech. Des.

Paper No: MD-24-1156

https://doi.org/10.1115/1.4066224

Published Online: August 16, 2024

View Article titled, Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Topics: Metamodeling, Modeling, Stress, Simulation, Tensors, Artificial intelligence, Artificial neural networks, Compression, Convolutional neural networks, Data-driven design

Journal Articles

Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data

Anindya Bhaduri, Nesar Ramachandra, Sandipp Krishnan Ravi, Lele Luan, Piyush Pandita, Prasanna Balaprakash, Mihai Anitescu, Changjie Sun, Liping Wang

Publisher: ASME

J. Comput. Inf. Sci. Eng. May 2024, 24(5): 051008.

Paper No: JCISE-23-1360

https://doi.org/10.1115/1.4064622

Published Online: March 5, 2024

View Article titled, Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data

Topics: Computer-aided engineering, Convolutional neural networks, Shapes, Stress

Proceedings Papers

Scalable Probabilistic Modeling and Machine Learning With Dimensionality Reduction for Expensive High-Dimensional Problems

Lele Luan, Nesar Ramachandra, Sandipp Krishnan Ravi, Anindya Bhaduri, Piyush Pandita, Prasanna Balaprakash, Mihai Anitescu, Changjie Sun, Liping Wang

Proc. ASME. IDETC-CIE2023, Volume 2: 43rd Computers and Information in Engineering Conference (CIE), V002T02A011, August 20–23, 2023

Publisher: American Society of Mechanical Engineers

Paper No: DETC2023-110704

https://doi.org/10.1115/DETC2023-110704

View Paper titled, Scalable Probabilistic Modeling and Machine Learning With Dimensionality Reduction for Expensive High-Dimensional Problems

Topics: Artificial neural networks, Machine learning, Metamodeling, Modeling

Proceedings Papers

Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data

Anindya Bhaduri, Nesar Ramachandra, Sandipp Krishnan Ravi, Lele Luan, Piyush Pandita, Prasanna Balaprakash, Mihai Anitescu, Changjie Sun, Liping Wang

Proc. ASME. IDETC-CIE2023, Volume 2: 43rd Computers and Information in Engineering Conference (CIE), V002T02A004, August 20–23, 2023

Publisher: American Society of Mechanical Engineers

Paper No: DETC2023-115077

https://doi.org/10.1115/DETC2023-115077

View Paper titled, Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data

Topics: Artificial neural networks, Shapes, Stress

Proceedings Papers

Towards Sparse and Interpretable Classification for Damage Assessment of Jet Engine Components

Panagiotis Tsilifis, Sandipp Krishnan Ravi, Feng Zhang, Piyush Pandita, Anindya Bhaduri, Ambarish Kulkarni, Liping Wang

Proc. ASME. GT2023, Volume 11B: Structures and Dynamics — Emerging Methods in Engineering Design, Analysis, and Additive Manufacturing; Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V11BT25A002, June 26–30, 2023

Publisher: American Society of Mechanical Engineers

Paper No: GT2023-100814

https://doi.org/10.1115/GT2023-100814

View Paper titled, Towards Sparse and Interpretable Classification for Damage Assessment of Jet Engine Components

Topics: Damage assessment, Jet engines, Machinery, Redundancy (Engineering)

Proceedings Papers

Reinforcement Learning Based Sequential Batch-Sampling for Bayesian Optimal Experimental Design

Yonatan Ashenafi, Piyush Pandita, Sayan Ghosh

Proc. ASME. IDETC-CIE2022, Volume 2: 42nd Computers and Information in Engineering Conference (CIE), V002T02A030, August 14–17, 2022

Publisher: American Society of Mechanical Engineers

Paper No: DETC2022-87995

https://doi.org/10.1115/DETC2022-87995

View Paper titled, Reinforcement Learning Based Sequential Batch-Sampling for Bayesian Optimal Experimental Design

Topics: Experimental design, Optimization, Reinforcement learning

Journal Articles

Special Issue: Integrated Design and Operation of Engineering Systems With Predictive Modeling

Pingfeng Wang, Sayan Ghosh, Yongming Liu, Zissimos P. Mourelatos, Jitesh Panchal, Piyush Pandita

Publisher: ASME

J. Mech. Des. September 2022, 144(9): 090301.

Paper No: MD-22-1470

https://doi.org/10.1115/1.4055120

Published Online: August 22, 2022

View Article titled, Special Issue: Integrated Design and Operation of Engineering Systems With Predictive Modeling

Topics: Design, Engineering systems and industry applications, Modeling

Journal Articles

Reinforcement Learning-Based Sequential Batch-Sampling for Bayesian Optimal Experimental Design

Yonatan Ashenafi, Piyush Pandita, Sayan Ghosh

Publisher: ASME

J. Mech. Des. September 2022, 144(9): 091705.

Paper No: MD-21-1780

https://doi.org/10.1115/1.4054631

Published Online: June 13, 2022

View Article titled, Reinforcement Learning-Based Sequential Batch-Sampling for Bayesian Optimal Experimental Design

Topics: Algorithms, Design, Experimental design, Optimization, Airfoils, Reinforcement learning

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

Publisher: ASME

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

Paper No: MD-21-1275

https://doi.org/10.1115/1.4052301

Published Online: September 21, 2021

View Article titled, Inverse Aerodynamic Design of Gas Turbine Blades Using Probabilistic Machine Learning

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

Publisher: American Society of Mechanical Engineers

Paper No: GT2021-58842

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

View Paper titled, A Probabilistic Machine Learning Framework for Explicit Inverse Design of Industrial Gas Turbine Blades

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

Publisher: ASME

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

Paper No: RISK-21-1010

https://doi.org/10.1115/1.4051748

Published Online: August 9, 2021

View Article titled, Application of Deep Transfer Learning and Uncertainty Quantification for Process Identification in Powder Bed Fusion

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

Publisher: ASME

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

Paper No: MD-20-1623

https://doi.org/10.1115/1.4050246

Published Online: March 24, 2021

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

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

Publisher: ASME

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

Paper No: RISK-19-1074

https://doi.org/10.1115/1.4046747

Published Online: May 12, 2020

View Article titled, Advances in Bayesian Probabilistic Modeling for Industrial Applications

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

Publisher: American Society of Mechanical Engineers

Paper No: DETC2019-97010

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

View Paper titled, Towards Scalable Gaussian Process Modeling

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

Publisher: ASME

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

Paper No: MD-18-1592

https://doi.org/10.1115/1.4043930

Published Online: July 10, 2019

View Article titled, Bayesian Optimal Design of Experiments for Inferring the Statistical Expectation of Expensive Black-Box Functions

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

Publisher: American Society of Mechanical Engineers

Paper No: DETC2018-85893

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

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

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

Publisher: American Society of Mechanical Engineers

Paper No: DETC2016-60527

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

View Paper titled, Extending Expected Improvement for High-Dimensional Stochastic Optimization of Expensive Black-Box Functions

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

Publisher: ASME

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

Paper No: MD-16-1215

https://doi.org/10.1115/1.4034104

Published Online: September 12, 2016

View Article titled, Extending Expected Improvement for High-Dimensional Stochastic Optimization of Expensive Black-Box Functions

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

Journal citation

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Manuscript

Industrial Applications of Transfer Learning Methods for Aircraft Propulsion Systems

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data

Scalable Probabilistic Modeling and Machine Learning With Dimensionality Reduction for Expensive High-Dimensional Problems

Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data

Towards Sparse and Interpretable Classification for Damage Assessment of Jet Engine Components

Reinforcement Learning Based Sequential Batch-Sampling for Bayesian Optimal Experimental Design

Special Issue: Integrated Design and Operation of Engineering Systems With Predictive Modeling

Reinforcement Learning-Based Sequential Batch-Sampling for Bayesian Optimal Experimental Design

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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