4 papers
Identifying changing partial differential equations using Sampled Local WeakIdent
Wenbo Hao, Mengyi Tang, Sung Ha Kang
We propose Sampled Local WeakIdent (SLW-Ident), a framework for identifying changing governing equations from a single set of given data. Different from a typical approach of using…
Neural Networks with Local Converging Inputs for Efficient Options Pricing Models
Harris Cobb, Wenbo Hao, Yingjie Liu
We present a novel application of Neural Networks with Local Converging Inputs (NNLCI) to improve the efficiency of existing numerical methods for pricing multi-asset options. The…
Symmetry-regularized neural ordinary differential equations
Wenbo Hao
Neural ordinary differential equations (Neural ODEs) is a class of machine learning models that approximate the time derivative of hidden states using a neural network. They are po…
Primal-dual hybrid gradient algorithms for computing time-implicit Hamilton-Jacobi equations
Tingwei Meng, Wenbo Hao, Siting Liu +2
Hamilton-Jacobi (HJ) partial differential equations (PDEs) have diverse applications spanning physics, optimal control, game theory, and imaging sciences. This research introduces…