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ORCIDDaniel Zhengyu Huang
Person information
- affiliation: California Institute of Technology, Pasadena, CA, USA
Other persons with the same name
- Daniel Huang 0001
![[0000-0002-1949-1116]](https://dblp.uni-trier.de./img/orcid-mark.12x12.png "0000-0002-1949-1116")
(aka: Dan E. Huang) — San Francisco State University, Language-Based Artificial Intelligence (LBAI) Lab, CA, USA (and 2 more) - Daniel Huang 0002 — National Tsing Hua University, Taiwan
- Daniel Huang 0003 — SAIC, Arlington, VA, USA
- Daniel Huang 0004 — ZTE Corporation
- Daniel Huang 0007 — Tsinghua University, Department of Automation, BNRist, Beijing, China
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2020 – today
- 2024
[i21]Daniel Zhengyu Huang, Nicholas H. Nelsen, Margaret Trautner:
An operator learning perspective on parameter-to-observable maps. CoRR abs/2402.06031 (2024)- [i20]Daniel Zhengyu Huang, Jiaoyang Huang, Zhengjiang Lin:
Convergence Analysis of Probability Flow ODE for Score-based Generative Models. CoRR abs/2404.09730 (2024) - [i19]Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart:
Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows. CoRR abs/2406.17263 (2024) - [i18]José A. Carrillo, Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Dongyi Wei:
Fisher-Rao Gradient Flow: Geodesic Convexity and Functional Inequalities. CoRR abs/2407.15693 (2024) - 2023
- [j8]Zongyi Li, Daniel Zhengyu Huang, Burigede Liu, Anima Anandkumar:
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries. J. Mach. Learn. Res. 24: 388:1-388:26 (2023) - [i17]Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart:
Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance. CoRR abs/2302.11024 (2023) - [i16]Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart:
Sampling via Gradient Flows in the Space of Probability Measures. CoRR abs/2310.03597 (2023) - [i15]Ziyuan Liu, Yuhang Wu, Daniel Zhengyu Huang, Hong Zhang, Xu Qian, Songhe Song:
SPFNO: Spectral operator learning for PDEs with Dirichlet and Neumann boundary conditions. CoRR abs/2312.06980 (2023) - 2022
- [j7]Daniel Zhengyu Huang, Tapio Schneider, Andrew M. Stuart:
Iterated Kalman methodology for inverse problems. J. Comput. Phys. 463: 111262 (2022) - [j6]Jiaoyang Huang, Daniel Zhengyu Huang, Qing Yang, Guang Cheng:
Power Iteration for Tensor PCA. J. Mach. Learn. Res. 23: 128:1-128:47 (2022) - [j5]Oliver R. A. Dunbar, Ignacio Lopez-Gomez, Alfredo Garbuno-Iñigo, Daniel Zhengyu Huang, Eviatar Bach, Jinlong Wu:
EnsembleKalmanProcesses.jl: Derivative-free ensemble-based model calibration. J. Open Source Softw. 7(80): 4869 (2022) - [c1]Luning Sun, Daniel Huang, Hao Sun, Jian-Xun Wang:
Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty. NeurIPS 2022 - [i14]Maarten V. de Hoop, Daniel Zhengyu Huang, Elizabeth Qian, Andrew M. Stuart:
The Cost-Accuracy Trade-Off In Operator Learning With Neural Networks. CoRR abs/2203.13181 (2022) - [i13]Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart:
Efficient Derivative-free Bayesian Inference for Large-Scale Inverse Problems. CoRR abs/2204.04386 (2022) - [i12]Zongyi Li, Daniel Zhengyu Huang, Burigede Liu, Anima Anandkumar:
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries. CoRR abs/2207.05209 (2022) - [i11]Luning Sun, Daniel Zhengyu Huang, Hao Sun, Jian-Xun Wang:
Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty. CoRR abs/2210.08095 (2022) - 2021
- [j4]Kailai Xu, Daniel Zhengyu Huang, Eric Darve:
Learning constitutive relations using symmetric positive definite neural networks. J. Comput. Phys. 428: 110072 (2021) - [i10]Daniel Zhengyu Huang, Tapio Schneider, Andrew M. Stuart:
Unscented Kalman Inversion. CoRR abs/2102.01580 (2021) - [i9]Daniel Zhengyu Huang, Jiaoyang Huang:
Improve Unscented Kalman Inversion With Low-Rank Approximation and Reduced-Order Model. CoRR abs/2102.10677 (2021) - [i8]Daniel Zhengyu Huang, Jiaoyang Huang:
Unscented Kalman Inversion: Efficient Gaussian Approximation to the Posterior Distribution. CoRR abs/2103.00277 (2021) - [i7]Shunxiang Cao, Daniel Zhengyu Huang:
Bayesian Calibration for Large-Scale Fluid Structure Interaction Problems Under Embedded/Immersed Boundary Framework. CoRR abs/2105.09497 (2021) - [i6]Gérard Ben Arous, Daniel Zhengyu Huang, Jiaoyang Huang:
Long Random Matrices and Tensor Unfolding. CoRR abs/2110.10210 (2021) - 2020
- [j3]Daniel Zhengyu Huang, Will Pazner, Per-Olof Persson, Matthew J. Zahr:
High-order partitioned spectral deferred correction solvers for multiphysics problems. J. Comput. Phys. 412: 109441 (2020) - [j2]Daniel Zhengyu Huang, Kailai Xu, Charbel Farhat, Eric Darve:
Learning constitutive relations from indirect observations using deep neural networks. J. Comput. Phys. 416: 109491 (2020) - [i5]Kailai Xu, Daniel Zhengyu Huang, Eric Darve:
Learning Constitutive Relations using Symmetric Positive Definite Neural Networks. CoRR abs/2004.00265 (2020) - [i4]Philip Avery, Daniel Zhengyu Huang, Wanli He, Johanna Ehlers, Armen Derkevorkian, Charbel Farhat:
A computationally tractable framework for nonlinear dynamic multiscale modeling of membrane fabric. CoRR abs/2007.05877 (2020)
2010 – 2019
- 2019
- [i3]Daniel Zhengyu Huang, Philip Avery, Charbel Farhat:
An Embedded Boundary Approach for Resolving the Contribution of Cable Subsystems to Fully Coupled Fluid-Structure Interaction. CoRR abs/1908.08382 (2019) - [i2]Daniel Zhengyu Huang, Will Pazner, Per-Olof Persson, Matthew J. Zahr:
High-order partitioned spectral deferred correction solvers for multiphysics problems. CoRR abs/1909.01977 (2019) - [i1]Daniel Zhengyu Huang, Philip Avery, Charbel Farhat, Jason Rabinovitch, Armen Derkevorkian, Lee D. Peterson:
Modeling, simulation and validation of supersonic parachute inflation dynamics during Mars landing. CoRR abs/1912.01658 (2019) - 2018
- [j1]Daniel Zhengyu Huang, Dante De Santis, Charbel Farhat:
A family of position- and orientation-independent embedded boundary methods for viscous flow and fluid-structure interaction problems. J. Comput. Phys. 365: 74-104 (2018)
Coauthor Index
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last updated on 2024-11-07 21:32 CET by the dblp team
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