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Journal Articles

Metacomputing for Directly Computable Multiphysics Models

John G. Michopoulos, Athanasios P. Iliopoulos, John C. Steuben, Nicoleta A. Apetre

Publisher: ASME

J. Comput. Inf. Sci. Eng. December 2023, 23(6): 060820.

Paper No: JCISE-23-1241

https://doi.org/10.1115/1.4063103

Published Online: September 14, 2023

View Article titled, Metacomputing for Directly Computable Multiphysics Models

Topics: Algebra, Anisotropy, Coordinate measuring machines, Modeling, Physics, Semantics, Tensors, Workflow, Computer software, Elasticity

Journal Articles

Accelerating Thermal Simulations in Additive Manufacturing by Training Physics-Informed Neural Networks With Randomly Synthesized Data

Jiangce Chen, Justin Pierce, Glen Williams, Timothy W. Simpson, Nicholas Meisel, Sneha Prabha Narra, Christopher McComb

J. Comput. Inf. Sci. Eng. January 2024, 24(1): 011004.

Paper No: JCISE-23-1058

https://doi.org/10.1115/1.4062852

Published Online: July 20, 2023

View Article titled, Accelerating Thermal Simulations in Additive Manufacturing by Training Physics-Informed Neural Networks With Randomly Synthesized Data

Topics: Artificial neural networks, Simulation, Physics, Temperature, Additive manufacturing

Journal Articles

Formal Qualitative Physics-Based Reasoning for Functional Decomposition of Engineered Systems

Xiaoyang Mao, Chiradeep Sen

J. Comput. Inf. Sci. Eng. February 2024, 24(2): 021003.

Paper No: JCISE-22-1240

https://doi.org/10.1115/1.4062748

Published Online: July 13, 2023

View Article titled, Formal Qualitative Physics-Based Reasoning for Functional Decomposition of Engineered Systems

Topics: Algorithms, Design, Flow (Dynamics), Physics, Computer software, Modeling, Topology, Natural language processing, Heating, Temperature

Journal Articles

A Physics-Driven Method for Determining Wheel—Rail Contact Area With Gradient-Based Optimization

Long Liu, Bing Yi, Daping Li

J. Comput. Inf. Sci. Eng. October 2023, 23(5): 051006.

Paper No: JCISE-22-1204

https://doi.org/10.1115/1.4056921

Published Online: March 29, 2023

View Article titled, A Physics-Driven Method for Determining Wheel—Rail Contact Area With Gradient-Based Optimization

Topics: Optimization, Rails, Wheels, Physics, Wear

Journal Articles

Multifidelity Physics-Constrained Neural Networks With Minimax Architecture

Dehao Liu, Pranav Pusarla, Yan Wang

J. Comput. Inf. Sci. Eng. June 2023, 23(3): 031008.

Paper No: JCISE-22-1159

https://doi.org/10.1115/1.4055316

Published Online: December 9, 2022

View Article titled, Multifidelity Physics-Constrained Neural Networks With Minimax Architecture

Topics: Artificial neural networks, Heat transfer, Phase transitions, Physics, Machine learning, Temperature, Modeling, Optimization

Journal Articles

Physics Informed Synthetic Image Generation for Deep Learning-Based Detection of Wrinkles and Folds

Omey M. Manyar, Junyan Cheng, Reuben Levine, Vihan Krishnan, Jernej Barbič, Satyandra K. Gupta

J. Comput. Inf. Sci. Eng. June 2023, 23(3): 030903.

Paper No: JCISE-22-1284

https://doi.org/10.1115/1.4056295

Published Online: December 9, 2022

View Article titled, Physics Informed Synthetic Image Generation for Deep Learning-Based Detection of Wrinkles and Folds

Topics: Composite materials, Failure, Flaw detection, Physics, Pipelines, Texture (Materials), Manufacturing, Simulation, Carbon fibers, Image segmentation

Journal Articles

Physics-Constrained Bayesian Neural Network for Bias and Variance Reduction

Luka Malashkhia, Dehao Liu, Yanglong Lu, Yan Wang

J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011012.

Paper No: JCISE-22-1123

https://doi.org/10.1115/1.4055924

Published Online: November 8, 2022

View Article titled, Physics-Constrained Bayesian Neural Network for Bias and Variance Reduction

Topics: Algorithms, Artificial neural networks, Heat transfer, Physics, Simulation, Temperature, Uncertainty, Phase transitions, Finite element methods, Additive manufacturing

Journal Articles

Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications

Anh Tran, Kathryn Maupin, Theron Rodgers

J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011011.

Paper No: JCISE-22-1017

https://doi.org/10.1115/1.4055852

Published Online: October 20, 2022

View Article titled, Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications

Topics: Machine learning, Materials science, Physics, Stress, Testing, Crystals, Plasticity, Simulation, Noise (Sound), Finite element analysis

Journal Articles

Acceleration of a Physics-Based Machine Learning Approach for Modeling and Quantifying Model-Form Uncertainties and Performing Model Updating

Marie-Jo Azzi, Chady Ghnatios, Philip Avery, Charbel Farhat

J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011009.

Paper No: JCISE-22-1149

https://doi.org/10.1115/1.4055546

Published Online: September 27, 2022

View Article titled, Acceleration of a Physics-Based Machine Learning Approach for Modeling and Quantifying Model-Form Uncertainties and Performing Model Updating

Topics: Approximation, Computer simulation, Construction, Dimensions, Machine learning, Modeling, Nozzles, Physics, Robustness, Simulation

Journal Articles

JCISE Editorial – August 2022

Yan Wang

J. Comput. Inf. Sci. Eng. August 2022, 22(4): 040201.

Paper No: JCISE-22-1198

https://doi.org/10.1115/1.4054864

Published Online: July 1, 2022

View Article titled, JCISE Editorial – August 2022

Topics: Additive manufacturing, Artificial intelligence, Augmented reality, Casting, China, Cities, Cloud computing, Collaboration, Complex systems, Computation

Journal Articles

Sub-Second Prediction of the Heatmap of Powder-Beds in Additive Manufacturing Using Deep Encoder–Decoder Convolutional Neural Networks

Ardalan R. Sofi, Bahram Ravani

J. Comput. Inf. Sci. Eng. April 2023, 23(2): 021008.

Paper No: JCISE-22-1035

https://doi.org/10.1115/1.4054559

Published Online: June 7, 2022

View Article titled, Sub-Second Prediction of the Heatmap of Powder-Beds in Additive Manufacturing Using Deep Encoder–Decoder Convolutional Neural Networks

Topics: Artificial neural networks, Lasers, Physics, Simulation, Additive manufacturing, Temperature, Manufacturing, Trajectories (Physics)

Journal Articles

Solving Inverse Heat Transfer Problems Without Surrogate Models: A Fast, Data-Sparse, Physics Informed Neural Network Approach

Vivek Oommen, Balaji Srinivasan

J. Comput. Inf. Sci. Eng. August 2022, 22(4): 041012.

Paper No: JCISE-21-1348

https://doi.org/10.1115/1.4053800

Published Online: March 10, 2022

View Article titled, Solving Inverse Heat Transfer Problems Without Surrogate Models: A Fast, Data-Sparse, Physics Informed Neural Network Approach

Topics: Artificial neural networks, Errors, Heat, Heat transfer, Physics, Temperature, Neural network models, Error analysis, Inverse problems, Machine learning

Journal Articles

Using Physics-Informed Generative Adversarial Networks to Perform Super-Resolution for Multiphase Fluid Simulations

Matthew Li, Christopher McComb

J. Comput. Inf. Sci. Eng. August 2022, 22(4): 044501.

Paper No: JCISE-21-1160

https://doi.org/10.1115/1.4053671

Published Online: February 21, 2022

View Article titled, Using Physics-Informed Generative Adversarial Networks to Perform Super-Resolution for Multiphase Fluid Simulations

Topics: Computational fluid dynamics, Fluids, Physics, Resolution (Optics), Simulation, Engineering simulation, Turbulence

Journal Articles

A Normal Equation-Based Extreme Learning Machine for Solving Linear Partial Differential Equations

Vikas Dwivedi, Balaji Srinivasan

J. Comput. Inf. Sci. Eng. February 2022, 22(1): 014502.

Paper No: JCISE-21-1079

https://doi.org/10.1115/1.4051530

Published Online: July 14, 2021

View Article titled, A Normal Equation-Based Extreme Learning Machine for Solving Linear Partial Differential Equations

Topics: Diffusion (Physics), Errors, Function approximation, Machinery, Partial differential equations, Physics, Artificial neural networks, Differential equations, Performance evaluation, Numerical analysis

Journal Articles

Hybrid Modeling Approach for Melt-Pool Prediction in Laser Powder Bed Fusion Additive Manufacturing

Tesfaye Moges, Zhuo Yang, Kevontrez Jones, Shaw Feng, Paul Witherell, Yan Lu

J. Comput. Inf. Sci. Eng. October 2021, 21(5): 050902.

Paper No: JCISE-20-1259

https://doi.org/10.1115/1.4050044

Published Online: May 12, 2021

View Article titled, Hybrid Modeling Approach for Melt-Pool Prediction in Laser Powder Bed Fusion Additive Manufacturing

Topics: Lasers, Modeling, Physics, Simulation, Computational fluid dynamics, Errors, Additive manufacturing, Polynomials

Journal Articles

Multiscale Tomographic Wave–Matter Interaction Modeling to Enable Artifact-Free Material Defect Reconstruction

John C. Steuben, John G. Michopoulos, Athanasios P. Iliopoulos, Benjamin D. Graber, Andrew J. Birnbaum

J. Comput. Inf. Sci. Eng. October 2021, 21(5): 050901.

Paper No: JCISE-20-1256

https://doi.org/10.1115/1.4049760

Published Online: May 10, 2021

View Article titled, Multiscale Tomographic Wave–Matter Interaction Modeling to Enable Artifact-Free Material Defect Reconstruction

Topics: Geometry, Modeling, Sensors, Simulation, Waves, X-rays, Errors, Algorithms, Wave equations, Computerized tomography

Journal Articles

Physics-Based Compressive Sensing to Enable Digital Twins of Additive Manufacturing Processes

Yanglong Lu, Eduard Shevtshenko, Yan Wang

J. Comput. Inf. Sci. Eng. June 2021, 21(3): 031009.

Paper No: JCISE-20-1239

https://doi.org/10.1115/1.4050377

Published Online: March 25, 2021

View Article titled, Physics-Based Compressive Sensing to Enable Digital Twins of Additive Manufacturing Processes

Topics: Additive manufacturing, Cooling, Physics, Temperature, Temperature distribution, Errors, Process monitoring, Gears

Journal Articles

Physics-Based Gaussian Process Method for Predicting Average Product Lifetime in Design Stage

Xinpeng Wei, Daoru Han, Xiaoping Du

J. Comput. Inf. Sci. Eng. August 2021, 21(4): 041006.

Paper No: JCISE-20-1217

https://doi.org/10.1115/1.4049509

Published Online: February 11, 2021

View Article titled, Physics-Based Gaussian Process Method for Predicting Average Product Lifetime in Design Stage

Topics: Design, Failure, Physics, Reliability, Stochastic processes, Event history analysis, Statistics, Testing, Errors, Experimental design

Journal Articles

A Performance Analysis of Phantom-Cell Adaptive Mesh Refinement on CPUs and GPUs

Daniel J. Dunning, Robert W. Robey, Jeffery A. Kuehn, Jeanine Cook

J. Comput. Inf. Sci. Eng. February 2021, 21(1): 011011.

Paper No: JCISE-20-1101

https://doi.org/10.1115/1.4048717

Published Online: December 10, 2020

View Article titled, A Performance Analysis of Phantom-Cell Adaptive Mesh Refinement on CPUs and GPUs

Topics: Graphics processing units, Phantoms, Physics, Optimization, Hardware

Journal Articles

Fault Adaptive Mission Planning: Increasing Useful-Life and Reducing Downtime Through Condition-Based Decision-Making

Charlie DeStefano, David Jensen

J. Comput. Inf. Sci. Eng. December 2020, 20(6): 061013.

Paper No: JCISE-19-1059

https://doi.org/10.1115/1.4047354

Published Online: June 22, 2020

View Article titled, Fault Adaptive Mission Planning: Increasing Useful-Life and Reducing Downtime Through Condition-Based Decision-Making

Topics: Complex systems, Failure, Maintenance, Robots, Service life (Equipment), Downtime, Decision making, Simulation, Physics

Journal citation

NARROW

Metacomputing for Directly Computable Multiphysics Models

Accelerating Thermal Simulations in Additive Manufacturing by Training Physics-Informed Neural Networks With Randomly Synthesized Data

Formal Qualitative Physics-Based Reasoning for Functional Decomposition of Engineered Systems

A Physics-Driven Method for Determining Wheel—Rail Contact Area With Gradient-Based Optimization

Multifidelity Physics-Constrained Neural Networks With Minimax Architecture

Physics Informed Synthetic Image Generation for Deep Learning-Based Detection of Wrinkles and Folds

Physics-Constrained Bayesian Neural Network for Bias and Variance Reduction

Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications

Acceleration of a Physics-Based Machine Learning Approach for Modeling and Quantifying Model-Form Uncertainties and Performing Model Updating

JCISE Editorial – August 2022

Sub-Second Prediction of the Heatmap of Powder-Beds in Additive Manufacturing Using Deep Encoder–Decoder Convolutional Neural Networks

Solving Inverse Heat Transfer Problems Without Surrogate Models: A Fast, Data-Sparse, Physics Informed Neural Network Approach

Using Physics-Informed Generative Adversarial Networks to Perform Super-Resolution for Multiphase Fluid Simulations

A Normal Equation-Based Extreme Learning Machine for Solving Linear Partial Differential Equations

Hybrid Modeling Approach for Melt-Pool Prediction in Laser Powder Bed Fusion Additive Manufacturing

Multiscale Tomographic Wave–Matter Interaction Modeling to Enable Artifact-Free Material Defect Reconstruction

Physics-Based Compressive Sensing to Enable Digital Twins of Additive Manufacturing Processes

Physics-Based Gaussian Process Method for Predicting Average Product Lifetime in Design Stage

A Performance Analysis of Phantom-Cell Adaptive Mesh Refinement on CPUs and GPUs

Fault Adaptive Mission Planning: Increasing Useful-Life and Reducing Downtime Through Condition-Based Decision-Making

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