About

Gregory J. Wagner is a Professor of Mechanical Engineering at Northwestern University and serves as the Director of Graduate Studies for Mechanical Engineering. He holds a Ph.D. and M.S. in Mechanical Engineering from Northwestern University and a B.S. in Mechanical Engineering from Boston University. His research interests focus on the development and application of computational simulation methods for multi-scale and multi-physics problems in engineering, particularly in fluid dynamics, heat transfer, and material transport. His work involves creating models for complex systems where multiple physical phenomena are coupled within a single domain or across interfaces, and solving these models using high-performance computing tools. His research includes applications such as melting and solidification in advanced manufacturing, fluid-structure interaction in biological systems, heat transfer in multi-phase flows, and multi-scale environmental transport.

Research topics

• Computer Science
• Machine Learning
• Artificial Intelligence
• Physics
• Mechanical engineering
• Algorithm
• Materials science
• Engineering
• Mechanics
• Mathematics
• Structural engineering
• Composite material
• Thermodynamics

Selected publications

• Multiscale coupling of numerical and data-driven models with HASTE: Hybrid algorithm using surrogate temperature evaluation

  Computer Methods in Applied Mechanics and Engineering · 2026-03-20

  articleSenior authorCorresponding
  PublisherDOI

• Efficient part-scale thermal modeling of laser powder bed fusion via a multilevel finite element framework

  Additive manufacturing · 2025-07-01 · 1 citations

  articleSenior authorCorresponding
  PublisherDOI

• A high-throughput physics- and data-driven framework for High-Entropy Alloy development

  Acta Materialia · 2025-04-27 · 5 citations

  articleSenior authorCorresponding
  PublisherDOI

• Advancing Aviation Safety Through Predictive Maintenance: Machine Learning Approach for Carbon Brake Wear Severity Classification

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Enhancing Model Generalizability in Aircraft Carbon Brake Wear Prediction: A Comparative Study and Transfer Learning Approach

  Aerospace · 2025-06-18 · 1 citations

  articleOpen accessSenior author

  Predictive maintenance in commercial aviation demands highly reliable and robust models, particularly for critical components like carbon brakes. This paper addresses two primary concerns in modeling carbon brake wear for distinct aircraft variants: (1) the choice between developing specialized models for individual aircraft types versus a unified, general model, and (2) the potential of transfer learning (TL) to boost model performance across diverse domains (e.g., aircraft types). We evaluate the trade-offs between predictive performance and computational efficiency by comparing specialized models tailored to specific aircraft types with a generalized model designed to predict continuous wear values across multiple aircraft types. Additionally, we explore the efficacy of TL in leveraging existing domain knowledge to enhance predictions in new, related contexts. Our findings demonstrate that a well-tuned generalized model supported by TL offers a viable approach to reducing model complexity and computational demands while maintaining robust and reliable predictive performance. The implications of this research extend beyond aviation, suggesting broader applications in component predictive maintenance where data-driven insights are crucial for operational efficiency and safety.

  PublisherOA PDFDOI

• Unifying machine learning and interpolation theory via interpolating neural networks

  Nature Communications · 2025-10-01 · 7 citations

  articleOpen access

  Computational science and engineering are shifting toward data-centric, optimization-based, and self-correcting solvers with artificial intelligence. This transition faces challenges such as low accuracy with sparse data, poor scalability, and high computational cost in complex system design. This work introduces Interpolating Neural Network (INN)-a network architecture blending interpolation theory and tensor decomposition. INN significantly reduces computational effort and memory requirements while maintaining high accuracy. Thus, it outperforms traditional partial differential equation (PDE) solvers, machine learning (ML) models, and physics-informed neural networks (PINNs). It also efficiently handles sparse data and enables dynamic updates of nonlinear activation. Demonstrated in metal additive manufacturing, INN rapidly constructs an accurate surrogate model of Laser Powder Bed Fusion (L-PBF) heat transfer simulation. It achieves sub-10-micrometer resolution for a 10 mm path in under 15 minutes on a single GPU, which is 5-8 orders of magnitude faster than competing ML models. This offers a new perspective for addressing challenges in computational science and engineering.

  PublisherOA PDFDOI

• Predictive Maintenance of Aircraft Braking Systems: A Machine Learning Approach to Clustering Brake Wear Patterns

  2025-01-03 · 4 citations

  articleSenior author

  The operational integrity and performance of aircraft braking systems are paramount to commercial aircraft safety and maintenance planning efficiency. Operational integrity refers to the reliability of the braking systems under various conditions, including the ability to withstand high temperatures and resist wear. High operational integrity means fewer unexpected failures, which leads to improved overall aircraft safety and reduced unplanned maintenance. Brake performance includes the effectiveness of braking during landing and taxiing as well as the system's responsiveness to pilot inputs. This study presents a thorough approach to understanding carbon brake pad degradation by benchmarking a suite of unsupervised Machine Learning (ML) clustering algorithms. The objective is to uncover distinct wear patterns and identify salient features differentiating varying degrees of wear. This effort leverages data that includes aircraft-specific parameters (such as aircraft weight), operational conditions (such as flight duration), and environmental factors (such as static air temperature) along with airport characteristics (such as runway length) observed across an airline's fleet of widebody aircraft variants. A Random Forest classifier is implemented to determine the most influential predictors of wear levels, providing a robust feature importance analysis. Methods including Principal Component Analysis (PCA) and an autoencoder are then leveraged to further reduce the dimensionality of the dataset. Various clustering techniques, including K-Means and Agglomerative Clustering, are considered and benchmarked with varying hyperparameter settings. These methods are applied without the knowledge of pre-assigned wear labels, ensuring an unbiased grouping based on intrinsic data characteristics. The performance of these algorithms is then quantitatively assessed using unsupervised evaluation metrics (e.g., Silhouette score) and supervised metrics (e.g., Rand Index) to gain insights from wear labels derived from the available wear pin parameter from the aircraft data. Categorical labels (i.e., High, Medium, or Low wear) are created by interpolating the wear pin signal and categorizing the degradation per flight into quantiles. The top features identified by Random Forest are then analyzed for differences across clusters. This iterative clustering process helps explain the data's intrinsic structure, revealing the foremost features indicative of brake wear. The findings have the potential to contribute to predictive maintenance strategies by enhancing the understanding of how various operating and environmental conditions impact carbon brake pad degradation.

  PublisherDOI

• Metal additive manufacturing simulation across length, time, and computing scales

  International Materials Reviews · 2025-12-11

  article

  Metal additive manufacturing (AM) offers a unique opportunity for production of advanced materials and complex geometries. However, variability in microstructure and properties challenges conventional approaches to design, process optimization, qualification, and materials selection. Modeling and simulation can improve understanding of AM processing and materials, but also poses major challenges for existing computational methods. Simultaneously, modern scientific computing hardware has become increasingly complex, most notably with the adoption of hybrid architectures such as Graphical Processing Units (GPUs). If appropriately utilized, emerging computational capabilities provide an opportunity to reveal new insight into AM processing and the resulting material structure and properties. In this review we describe the computational AM landscape, identify critical gaps, and highlight opportunities to impact the development and application of AM. First, the requirements and challenges of representative AM problem statements will be defined. These problems range from scientific studies to industrial applications and are designed to capture the breadth of challenges facing the AM community. Next, the current state of AM modeling and simulation is evaluated, broken down by enabling hardware and software, process simulation, microstructure simulation, and property simulation. Each section describes the diversity of simulation approaches and associated trade-offs in physical fidelity and computational expense. Each area is then assessed based on their suitability and readiness for current and developing computational architectures. Lastly, the greatest opportunities for future research and application are highlighted, including gaps in modeling capabilities, opportunities for near-term application, and key scientific challenges.

  PublisherDOI

• Advancing Aviation Safety Through Predictive Maintenance: Machine Learning Approach for Carbon Brake Wear Severity Classification

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  preprintOpen accessSenior author
  PublisherDOI

• Advancing Aviation Safety Through Predictive Maintenance: A Machine Learning Approach for Carbon Brake Wear Severity Classification

  Aerospace · 2025-07-01 · 2 citations

  articleOpen accessSenior author

  Braking systems are essential to aircraft safety and operational efficiency; however, the variability of carbon brake wear, driven by the intricate interplay of operational and environmental factors, presents challenges for effective maintenance planning. This effort leverages machine learning classifiers to predict wear severity using operational data from an airline’s wide-body fleet equipped with wear pin sensors that measure the percentage of carbon pad remaining on each brake. Aircraft-specific metrics from flight data are augmented with weather and airport parameters from FlightAware® to better capture the operational environment. Through a systematic benchmarking of multiple classifiers, combined with structured hyperparameter tuning and uncertainty quantification, LGBM and Decision Tree models emerge as top performers, achieving predictive accuracies of up to 98.92%. The inclusion of environmental variables substantially improves model performance, with relative humidity and wind direction identified as key predictors. While machine learning has been extensively applied to predictive maintenance contexts, this work advances the field of brake wear prediction by integrating a comprehensive dataset that incorporates operational, environmental, and airport-specific features. In doing so, it addresses a notable gap in the existing literature regarding the impact of contextual variables on brake wear prediction.

  PublisherDOI

Frequent coauthors

• Wing Kam Liu

  46 shared

• Timothy J. Carrig

  Lockheed Martin (United States)

  31 shared

• Stephen Lin

  17 shared

• Glenn T. Bennett

  16 shared

• Andrew Malm

  14 shared

• W. J. Alford

  Atrium Medical (Australia)

  13 shared

• Wentao Yan

  13 shared

• Berton E. Callicoatt

  Lockheed Martin (United States)

  12 shared

Awards & honors

• Gregory Wagner Named Bette and Neison Harris Chair in Teachi…
• Four Professors Included in Special Nature Materials Themati…