Scientific Machine Learning Applications

Scientific Machine Learning Applications

Applying principled generative methods to scientific computing challenges.

Optimal Control

Framework for understanding and improving neural architectures through control theory.

Operator Learning

Probabilistic approaches to learning differential operators for physical systems.

Monte Carlo Methods

Rare event simulation and uncertainty quantification for complex systems.

More content coming soon…