Zobrazeno 1 - 10
of 1 834
pro vyhledávání: '"An, Zongren"'
Autor:
Toscano, Juan Diego, Oommen, Vivek, Varghese, Alan John, Zou, Zongren, Daryakenari, Nazanin Ahmadi, Wu, Chenxi, Karniadakis, George Em
Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sparse measurements. Over the pa
Externí odkaz:
http://arxiv.org/abs/2410.13228
The interplay between stochastic processes and optimal control has been extensively explored in the literature. With the recent surge in the use of diffusion models, stochastic processes have increasingly been applied to sample generation. This paper
Externí odkaz:
http://arxiv.org/abs/2409.09614
When predicting physical phenomena through simulation, quantification of the total uncertainty due to multiple sources is as crucial as making sure the underlying numerical model is accurate. Possible sources include irreducible aleatoric uncertainty
Externí odkaz:
http://arxiv.org/abs/2408.07201
Autor:
Shukla, Khemraj, Zou, Zongren, Chan, Chi Hin, Pandey, Additi, Wang, Zhicheng, Karniadakis, George Em
Multiphysics problems that are characterized by complex interactions among fluid dynamics, heat transfer, structural mechanics, and electromagnetics, are inherently challenging due to their coupled nature. While experimental data on certain state var
Externí odkaz:
http://arxiv.org/abs/2407.21217
Kolmogorov-Arnold Networks (KANs) were recently introduced as an alternative representation model to MLP. Herein, we employ KANs to construct physics-informed machine learning models (PIKANs) and deep operator models (DeepOKANs) for solving different
Externí odkaz:
http://arxiv.org/abs/2406.02917
Autor:
Yang, Runzhao, Chen, Yinda, Zhang, Zhihong, Liu, Xiaoyu, Li, Zongren, He, Kunlun, Xiong, Zhiwei, Suo, Jinli, Dai, Qionghai
In the field of medical image compression, Implicit Neural Representation (INR) networks have shown remarkable versatility due to their flexible compression ratios, yet they are constrained by a one-to-one fitting approach that results in lengthy enc
Externí odkaz:
http://arxiv.org/abs/2405.16850
Autor:
Zou, Zongren, Kahana, Adar, Zhang, Enrui, Turkel, Eli, Ranade, Rishikesh, Pathak, Jay, Karniadakis, George Em
We extend a recently proposed machine-learning-based iterative solver, i.e. the hybrid iterative transferable solver (HINTS), to solve the scattering problem described by the Helmholtz equation in an exterior domain with a complex absorbing boundary
Externí odkaz:
http://arxiv.org/abs/2405.12380
Uncertainty quantification (UQ) in scientific machine learning (SciML) combines the powerful predictive power of SciML with methods for quantifying the reliability of the learned models. However, two major challenges remain: limited interpretability
Externí odkaz:
http://arxiv.org/abs/2404.08809
Uncertainty quantification (UQ) in scientific machine learning (SciML) becomes increasingly critical as neural networks (NNs) are being widely adopted in addressing complex problems across various scientific disciplines. Representative SciML models a
Externí odkaz:
http://arxiv.org/abs/2311.11262
We address two major challenges in scientific machine learning (SciML): interpretability and computational efficiency. We increase the interpretability of certain learning processes by establishing a new theoretical connection between optimization pr
Externí odkaz:
http://arxiv.org/abs/2311.07790