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Sandipp Krishnan Ravi
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Journal Articles

Special Issue: Scientific Machine Learning for Manufacturing Processes and Material Systems

Accepted Manuscript
John Michopoulos, Anindya Bhaduri, Francisco Chinesta, Elias Cueto, Dehao Liu, Sandipp Krishnan Ravi, Jian-Xun Wang
Journal: Journal of Computing and Information Science in Engineering
Publisher: ASME
Article Type: Guest Editorial
J. Comput. Inf. Sci. Eng.
Paper No: JCISE-24-1524
https://doi.org/10.1115/1.4066791
Published Online: October 4, 2024
View Article titled, Special Issue: Scientific Machine Learning for Manufacturing Processes and Material Systems
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Topics: Machine learning, Manufacturing, Artificial intelligence, Computer-aided engineering, Computer-aided manufacturing, Engineering systems and industry applications
Journal Articles

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

Accepted Manuscript
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.
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
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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
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 Article titled, 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

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

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

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)
eBook Chapter

Comparison of Two Damage Parameters for Nonproportional Multiaxial Fatigue Assessment of Welded Structures

Series: ASTM Selected Technical Papers
Publisher: ASTM International
Published: 2023
ISBN: 978-0-8031-7733-8
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