125 citations · 142 across the 7 of their papers we have counts for
7 papers
Nested Fourier-enhanced neural operator for efficient modeling of radiation transfer in fires
Anran Jiao, Wengyao Jiang, Xiaoyi Lu +2
Computational fluid dynamics (CFD) has become an essential tool for predicting fire behavior, yet maintaining both efficiency and accuracy remains challenging. A major source of co…
TANTE: Time-Adaptive Operator Learning via Neural Taylor Expansion
Zhikai Wu, Sifan Wang, Shiyang Zhang +5
Operator learning for time-dependent partial differential equations (PDEs) has seen rapid progress in recent years, enabling efficient approximation of complex spatiotemporal dynam…
Quantum DeepONet: Neural operators accelerated by quantum computing
Pengpeng Xiao, Muqing Zheng, Anran Jiao +2
In the realm of computational science and engineering, constructing models that reflect real-world phenomena requires solving partial differential equations (PDEs) with different c…
Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operators
Anran Jiao, Qile Yan, Jhn Harlim +1
In this paper, we evaluate the effectiveness of deep operator networks (DeepONets) in solving both forward and inverse problems of partial differential equations (PDEs) on unknown…
Deep Learning for Solving and Estimating Dynamic Macro-Finance Models
Benjamin Fan, Edward Qiao, Anran Jiao +3
We develop a methodology that utilizes deep learning to simultaneously solve and estimate canonical continuous-time general equilibrium models in financial economics. We illustrate…
Reliable extrapolation of deep neural operators informed by physics or sparse observations
Min Zhu, Handi Zhang, Anran Jiao +2
Deep neural operators can learn nonlinear mappings between infinite-dimensional function spaces via deep neural networks. As promising surrogate solvers of partial differential equ…
One-shot learning for solution operators of partial differential equations
Anran Jiao, Haiyang He, Rishikesh Ranade +2
Learning and solving governing equations of a physical system, represented by partial differential equations (PDEs), from data is a central challenge in a variety of areas of scien…