Publications (7)
Dynamic Mortality Forecasting via Mixed-Frequency State-Space Models
Runze Li, Rui Zhou, David Pitt
High-frequency death counts are now widely available and contain timely information about intra-year mortality dynamics, but most stochastic mortality models are still estimated on…
Enabling Automatic Differentiation with Mollified Graph Neural Operators
Ryan Y. Lin, Julius Berner, Valentin Duruisseaux +5
Physics-informed neural operators offer a powerful framework for learning solution operators of partial differential equations (PDEs) by combining data and physics losses. However,…
Optimising pandemic response through vaccination strategies using neural networks
Chang Zhai, Ping Chen, Zhuo Jin +1
Epidemic risk assessment poses inherent challenges, with traditional approaches often failing to balance health outcomes and economic constraints. This paper presents a data-driven…
A Library for Learning Neural Operators
Jean Kossaifi, Nikola Kovachki, Zongyi Li +8
We present NeuralOperator, an open-source Python library for operator learning. Neural operators generalize neural networks to maps between function spaces instead of finite-dimens…
A comparative analysis of several multivariate zero-inflated and zero-modified models with applications in insurance
Pengcheng Zhang, David Pitt, Xueyuan Wu
Claim frequency data in insurance records the number of claims on insurance policies during a finite period of time. Given that insurance companies operate with multiple lines of i…
A model sufficiency test using permutation entropy
Xin Huang, Han Lin Shang, David Pitt
Using the ordinal pattern concept in permutation entropy, we propose a model sufficiency test to study a given model's point prediction accuracy. Compared to some classical model s…