Stephen R. Niezgoda
Materials Science · The Ohio State University
Publications
120
Citations
4,126
Est. group size
~4
Recurring co-author estimate
Active years
34
Publishing since 1993
Stephen R. Niezgoda's research focuses on understanding and predicting how the internal structure of metals (their 'microstructure') changes during processing and affects their mechanical properties. His recent work combines traditional materials science methods like crystal plasticity modeling with modern computational tools, including machine learning and GPU-accelerated simulations, to model and predict how metals deform and evolve at the microscopic scale. This work has applications ranging from understanding aluminum alloys to designing medical implants.
Publication output has fluctuated over the last decade with a peak around 2018, a dip in 2022, and a recent resurgence in 2025-2026, averaging under 7 papers per year over the last 5 years.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Training variation of physically-informed deep learning models
Machine Learning Science and Technology · 2026
- Mapping Microstructure: Manifold Construction for Accelerated Materials Exploration
Integrating materials and manufacturing innovation · 2026
- mesoOSU/PB-GAN: PBGAN: Model Variation
Zenodo (CERN European Organization for Nuclear Research) · 2026
- mesoOSU/PB-GAN: PBGAN: Model Variation
Zenodo (CERN European Organization for Nuclear Research) · 2026
- Author response for "Training variation of physically-informed deep learning models"
2026
- Author response for "Training variation of physically-informed deep learning models"
2026
- Efficient GPU-computing simulation platform JAX-CPFEM for differentiable crystal plasticity finite element method
npj Computational Materials · 2025
- Importance of Hyper-Parameter Optimization During Training of Physics-Informed Deep Learning Networks
Integrating materials and manufacturing innovation · 2025
- Engineered Porosity for Stiffness-Matched, Pbf-Lb, Nickel-Titanium Mandibular Graft Fixation Plates
SSRN Electronic Journal · 2025
- A Unified Mesoscale Framework for Predicting the Orientation-Dependent Substructure Evolution in FCC Metals
SSRN Electronic Journal · 2025
- Full-field elastic strain tensor evolution of 3D polycrystals with recurrent neural networks and transfer learning
Mechanics of Materials · 2025
- A unified mesoscale framework for predicting the orientation-dependent substructure evolution in FCC metals
Scripta Materialia · 2025
- An Efficient Graphical Processing Unit-Accelerated Calibration of Crystal Plasticity Model Parameters by Multi-Objective Optimization With Automatic Differentiation-Based Sensitivities
Journal of Applied Mechanics · 2025
- Engineered porosity for stiffness-matched, PBF-LB, Nickel-Titanium mandibular graft fixation plates
Materials & Design · 2025
- Efficient GPU-computing simulation platform JAX-PF for differentiable phase field model
arXiv (Cornell University) · 2025
- Integrating materials and manufacturing innovation×9
- International Journal of Plasticity×8
- SSRN Electronic Journal×8
- arXiv (Cornell University)×8
- Acta Materialia×7
- Chaitali S. Patil
Materials Science · The Ohio State University
- Shengfeng Yang
Materials Science · Purdue University West Lafayette
- Saeed Bagherzadeh
Engineering · Purdue University West Lafayette
- Marisol Koslowski
Materials Science · Purdue University West Lafayette
- Nicholas A. Richter
Materials Science · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 19, 2026.
Claim or correct this profile