project_topics.md

Project topics

1. Engineering:

   • Design optimization:

     • Materials design
     • Molecule design
     • Multi-disciplinary design

   • Model discovery:

     • Neural differential equations
     • Sparse regression
     • Hidden physics models

   • Forecasting and control of dynamical systems:

     • Model Predictive Control
     • Reinforcement learning

   • Modeling and simulation of continuum systems:

     • Solid mechanics
     • Fluid dynamics
     • Multi-scale/multi-physics systems

   • Modeling and simulation of molecular systems:

     • Sampling equilibrium states of many-body systems with deep learning
     • End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics
     • Coarse graining
     • Protein folding

   • Information fusion and multi-fidelity modeling:

     • Accelerating the prediction of large-scale computational models

2. Topics in Machine Learning:

   • Optimization:

     • Accelerated gradient methods
     • Proximal gradient methods

   • Physics-informed machine learning

   • Semi-supervised and self-supervised learning

   • Bayesian deep learning

3. Applications:

   • Filtering/de-noising/super-resolution of medical data

   • Clustering and classification

   • Climate modeling