A5008527954- Machine Learning in Materials Science
- Model Reduction and Neural Networks
- X-ray Diffraction in Crystallography
- Topology Optimization in Engineering
- Ultrasonics and Acoustic Wave Propagation
- Laser and Thermal Forming Techniques
- Metal Forming Simulation Techniques
- High Temperature Alloys and Creep
- Generative Adversarial Networks and Image Synthesis
- Neural Networks and Applications
- Advanced Multi-Objective Optimization Algorithms
- Electron and X-Ray Spectroscopy Techniques
- Rock Mechanics and Modeling
- Magnetic Properties and Applications
- Composite Structure Analysis and Optimization
- Numerical methods in engineering
Georgia Institute of Technology
2021-2024
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Engineering problems frequently require solution of governing equations with spatially-varying discontinuous coefficients. Even for linear elliptic problems, mapping large ensembles coefficient fields to solutions can become a major computational bottleneck using traditional numerical solvers. Furthermore, machine learning methods such as neural operators struggle fit these maps due sharp transitions and high contrast in the scarcity informative training data. In this work, we focus on...