Physics-Informed Neural Operator for Learning Partial Differential Equations
- Zongyi Li,
- Hongkai Zheng,
- Nikola Kovachki,
- David Jin,
- Haoxuan Chen,
- Burigede Liu,
- Kamyar Azizzadenesheli,
- Anima Anandkumar
In this article, we propose physics-informed neural operators (PINO) that combine training data and physics constraints to learn the solution operator of a given family of parametric Partial Differential Equations (PDE). PINO is the first hybrid approach ...
HighlightsPROBLEM STATEMENT
Machine learning methods have recently shown promise in solving partial differential equations (PDEs) raised in science and engineering. They can be classified into two broad categories: approximating the solution function ...
Anytime-valid off-policy Inference for Contextual Bandits
Contextual bandit algorithms are ubiquitous tools for active sequential experimentation in healthcare and the tech industry. They involve online learning algorithms that adaptively learn policies over time to map observed contexts Xt to actions Atin an ...
HighlightsPROBLEM STATEMENT
Contextual bandits and adaptive experimentation are becoming increasingly commonplace in the tech industry and health sciences. The problem setting consists of (at each time t) observing a context Xt, taking a randomized ...
The Necessity of Machine Learning Theory in Mitigating AI Risk
SUMMARY
In the last years we have witnessed rapidly accelerating progress in Neural Network-based Artificial Intelligence. Yet our fundamental understanding of these methods has lagged far behind. Never before had a technology been developed ...
