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Applied Mathematics

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Applied Mathematics

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Research Frontiers in Machine Learning for Inverse Problems

Development of neural network-based approaches to solve ill-posed inverse problems in imaging, tomography, and signal reconstruction.

Learned Regularization Landscapes in Ill-Posed Reconstruction
Neural Operators for Parametric Inverse Problem Families
Uncertainty Quantification Through Generative Model Priors
Physics-Informed Latent Space Exploration and Navigation
Adversarial Robustness in Inverse Problem Solvers
Implicit Differentiation for Constrained Inverse Imaging
Unrolled Algorithms Meet Transformer Architectures
Manifold Learning in High-Dimensional Inverse Spaces
Data-Driven Operator Inversion Without Ground Truth
Federated Learning for Distributed Inverse Problem Inference

All Applied Mathematics PhD categories