182 citations · 462 across the 20 of their papers we have counts for
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Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty
Luning Sun, Daniel Zhengyu Huang, Hao Sun +1
Nonlinear dynamics are ubiquitous in science and engineering applications, but the physics of most complex systems is far from being fully understood. Discovering interpretable gov…
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries
Zongyi Li, Daniel Zhengyu Huang, Burigede Liu +1
Deep learning surrogate models have shown promise in solving partial differential equations (PDEs). Among them, the Fourier neural operator (FNO) achieves good accuracy, and is sig…
Efficient Derivative-free Bayesian Inference for Large-Scale Inverse Problems
Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich +1
We consider Bayesian inference for large scale inverse problems, where computational challenges arise from the need for repeated evaluations of an expensive forward model. This ren…
The Cost-Accuracy Trade-Off In Operator Learning With Neural Networks
Maarten V. de Hoop, Daniel Zhengyu Huang, Elizabeth Qian +1
The term `surrogate modeling' in computational science and engineering refers to the development of computationally efficient approximations for expensive simulations, such as thos…