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Piyush Pandita
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Journal Articles

Heterogeneous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Process

Yigitcan Comlek, Sandipp Krishnan Ravi, Piyush Pandita, Sayan Ghosh, Liping Wang, Wei Chen
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. April 2025, 147(4): 041711.
Paper No: MD-24-1500
https://doi.org/10.1115/1.4068016
Published Online: March 13, 2025
View Articletitled, Heterogeneous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Process
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Topics: Data fusion, Modeling, Design, Manufacturing, Cantilever beams
Proceedings Papers

Heterogeneous Transfer Learning for Design and Manufacturing Process Modeling Through Input Mapping and Multi-Fidelity Gaussian Process

Sandipp Krishnan Ravi, Piyush Pandita, Changjie Sun, Liping Wang
Proc. ASME. IDETC-CIE2024, Volume 2A: 44th Computers and Information in Engineering Conference (CIE), V02AT02A031, August 25–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: DETC2024-143998
https://doi.org/10.1115/DETC2024-143998
View Paper titled, Heterogeneous Transfer Learning for Design and Manufacturing Process Modeling Through Input Mapping and Multi-Fidelity Gaussian Process
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Topics: Design, Manufacturing, Modeling, Optimization, Cantilever beams, Cycles, Design engineering, Engineering design, Friction, Machine learning
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
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. March 2025, 147(3): 031701.
Paper No: MD-24-1156
https://doi.org/10.1115/1.4066224
Published Online: October 18, 2024
View Articletitled, Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields
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Topics: Compression, Computer-aided engineering, Metamodeling, Modeling, Stress
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
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Topics: Aircraft propulsion, Design, Maintenance, Propulsion, Blades, Machine learning, Artificial neural networks, Cycles, Deep neural networks, Downtime
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
Journal: Journal of Computing and Information Science in Engineering
Publisher: ASME
Article Type: Research Papers
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 Articletitled, Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data
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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
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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
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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
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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
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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
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Guest Editorial
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 Articletitled, Special Issue: Integrated Design and Operation of Engineering Systems With Predictive Modeling
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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
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Research Papers
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 Articletitled, Reinforcement Learning-Based Sequential Batch-Sampling for Bayesian Optimal Experimental Design
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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
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Research Papers
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 Articletitled, Inverse Aerodynamic Design of Gas Turbine Blades Using Probabilistic Machine Learning
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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
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
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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
Journal: ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
Publisher: ASME
Article Type: Research-Article
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 Articletitled, Application of Deep Transfer Learning and Uncertainty Quantification for Process Identification in Powder Bed Fusion
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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
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Technical Briefs
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 Articletitled, Scalable Fully Bayesian Gaussian Process Modeling and Calibration With Adaptive Sequential Monte Carlo for Industrial Applications
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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
Journal: ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
Publisher: ASME
Article Type: Research-Article
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 Articletitled, Advances in Bayesian Probabilistic Modeling for Industrial Applications
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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
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
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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
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Research-Article
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 Articletitled, Bayesian Optimal Design of Experiments for Inferring the Statistical Expectation of Expensive Black-Box Functions
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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
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
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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
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
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Topics: Optimization, Uncertainty, Approximation, Design, Chain, Engineering simulation, Filters, Measurement uncertainty, Particulate matter, Permeability