1 code implementation • 2 Feb 2024 • Jinyuan Chang, Zhao Ding, Yuling Jiao, Ruoxuan Li, Jerry Zhijian Yang
We introduce an Ordinary Differential Equation (ODE) based deep generative method for learning a conditional distribution, named the Conditional Follmer Flow.
no code implementations • 9 Jan 2024 • Zhao Ding, Chenguang Duan, Yuling Jiao, Jerry Zhijian Yang
We propose SDORE, a semi-supervised deep Sobolev regressor, for the nonparametric estimation of the underlying regression function and its gradient.
no code implementations • 11 Oct 2023 • Zhan Yu, Qiuhao Chen, Yuling Jiao, Yinan Li, Xiliang Lu, Xin Wang, Jerry Zhijian Yang
To achieve this, we utilize techniques from quantum signal processing and linear combinations of unitaries to construct PQCs that implement multivariate polynomials.
no code implementations • 24 Jun 2023 • Chenguang Duan, Yuling Jiao, Xiliang Lu, Jerry Zhijian Yang
In this paper, we introduce CDII-PINNs, a computationally efficient method for solving CDII using PINNs in the framework of Tikhonov regularization.
no code implementations • 28 Mar 2023 • Yuling Jiao, Di Li, Xiliang Lu, Jerry Zhijian Yang, Cheng Yuan
With the recent study of deep learning in scientific computation, the Physics-Informed Neural Networks (PINNs) method has drawn widespread attention for solving Partial Differential Equations (PDEs).
no code implementations • 28 Feb 2021 • Yuling Jiao, Yanming Lai, Xiliang Lu, Fengru Wang, Jerry Zhijian Yang, Yuanyuan Yang
In this paper, we construct neural networks with ReLU, sine and $2^x$ as activation functions.