Lu Lu - RankMe

50 papers Journal 49Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Active operator learning with predictive uncertainty quantification for partial differential equations. J. Comput. Phys. Nick Winovich, Mitchell Daneker, Lu Lu, Guang Lin
2026	J	`jnl`	DeepONet as a multi-Operator extrapolation model: Distributed pretraining with physics-Informed fine-Tuning. J. Comput. Phys. Zecheng Zhang, Christian Moya, Lu Lu, Guang Lin, Hayden Schaeffer
2026	J	`jnl`	Learning multi-physical system on a unified manifold by collaboratively fused features. Eng. Appl. Artif. Intell. Linzheng Wang, Zituo Chen, Yaojun Li, Ruiqu Deng, Ruizhi Zhang, Yonghao Luo, Lu Lu, Sili Deng
2026	J	`jnl`	Solving Forward and Inverse Partial Differential Equation Problems on Unknown Manifolds via Physics-Informed Neural Operators. SIAM J. Sci. Comput. Anran Jiao, Qile Yan, John Harlim, Lu Lu
2025	J	`jnl`	Active operator learning with predictive uncertainty quantification for partial differential equations. CoRR Nick Winovich, Mitchell Daneker, Lu Lu, Guang Lin
2025	J	`jnl`	Federated Scientific Machine Learning for Approximating Functions and Solving Differential Equations With Data Heterogeneity. IEEE Trans. Neural Networks Learn. Syst. Handi Zhang, Langchen Liu, Kangyu Weng, Lu Lu
2025	J	`jnl`	From imaging to computational domains for physics-driven molecular biology simulations: Hindered diffusion in platelet masses. PLoS Comput. Biol. Catherine House, Ziyi Huang, Kaushik N. Shankar, Sandra J. Young, Meghan E. Roberts, Scott L. Diamond, Maurizio Tomaiuolo, Timothy J. Stalker, Lu Lu, Talid Sinno
2025	J	`jnl`	PI-MFM: Physics-informed multimodal foundation model for solving partial differential equations. CoRR Min Zhu, Jingmin Sun, Zecheng Zhang, Hayden Schaeffer, Lu Lu
2025	J	`jnl`	Quantum DeepONet: Neural operators accelerated by quantum computing. Quantum Pengpeng Xiao, Muqing Zheng, Anran Jiao, Xiu Yang, Lu Lu
2024	J	`jnl`	A scalable framework for learning the geometry-dependent solution operators of partial differential equations. Nat. Comput. Sci. Minglang Yin, Nicolas Charon, Ryan Brody, Lu Lu, Natalia A. Trayanova, Mauro Maggioni
2024	J	`jnl`	ADEPT: A Noninvasive Method for Determining Elastic Parameters of Valve Tissue. CoRR Wensi Wu, Mitchell Daneker, Christian Herz, Hannah Dewey, Jeffrey A. Weiss, Alison M. Pouch, Lu Lu, Matthew A. Jolley
2024	J	`jnl`	Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks. CoRR Christian Moya, Amirhossein Mollaali, Zecheng Zhang, Lu Lu, Guang Lin
2024	J	`jnl`	DIMON: Learning Solution Operators of Partial Differential Equations on a Diffeomorphic Family of Domains. CoRR Minglang Yin, Nicolas Charon, Ryan Brody, Lu Lu, Natalia A. Trayanova, Mauro Maggioni
2024	J	`jnl`	DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning. CoRR Zecheng Zhang, Christian Moya, Lu Lu, Guang Lin, Hayden Schaeffer
2024	J	`jnl`	Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration. Reliab. Eng. Syst. Saf. Zhongyi Jiang, Min Zhu, Lu Lu
2024	J	`jnl`	Identifying heterogeneous micromechanical properties of biological tissues via physics-informed neural networks. CoRR Wensi Wu, Mitchell Daneker, Kevin T. Turner, Matthew A. Jolley, Lu Lu
2024	J	`jnl`	PCDMD: Physics-constrained dynamic mode decomposition for accurate and robust forecasting of dynamical systems with imperfect data and physics. Comput. Phys. Commun. Yuhui Yin, Chenhui Kou, Shengkun Jia, Lu Lu, Xigang Yuan, Yiqing Luo
2024	J	`jnl`	Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operators. CoRR Anran Jiao, Qile Yan, John Harlim, Lu Lu
2023	J	`jnl`	D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators. CoRR Zecheng Zhang, Christian Moya, Lu Lu, Guang Lin, Hayden Schaeffer
2023	J	`jnl`	Deep Learning for Solving and Estimating Dynamic Macro-Finance Models. CoRR Benjamin Fan, Edward Qiao, Anran Jiao, Zhouzhou Gu, Wenhao Li, Lu Lu
2023	J	`jnl`	Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration. CoRR Zhongyi Jiang, Min Zhu, Dongzhuo Li, Qiuzi Li, Yanhua O. Yuan, Lu Lu
2023	J	`jnl`	Neural operator prediction of linear instability waves in high-speed boundary layers. J. Comput. Phys. Patricio Clark Di Leoni, Lu Lu, Charles Meneveau, George Em Karniadakis, Tamer A. Zaki
2022	J	`jnl`	Approximation rates of DeepONets for learning operators arising from advection-diffusion equations. Neural Networks Beichuan Deng, Yeonjong Shin, Lu Lu, Zhongqiang Zhang, George Em Karniadakis
2022	J	`jnl`	Effective Data Sampling Strategies and Boundary Condition Constraints of Physics-Informed Neural Networks for Identifying Material Properties in Solid Mechanics. CoRR Wensi Wu, Mitchell Daneker, Matthew A. Jolley, Kevin T. Turner, Lu Lu
2022	J	`jnl`	MIONet: Learning Multiple-Input Operators via Tensor Product. SIAM J. Sci. Comput. Pengzhan Jin, Shuai Meng, Lu Lu
2022	J	`jnl`	MIONet: Learning multiple-input operators via tensor product. CoRR Pengzhan Jin, Shuai Meng, Lu Lu
2022	J	`jnl`	Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport. CoRR Lu Lu, Raphaël Pestourie, Steven G. Johnson, Giuseppe Romano
2022	J	`jnl`	Reliable extrapolation of deep neural operators informed by physics or sparse observations. CoRR Min Zhu, Handi Zhang, Anran Jiao, George Em Karniadakis, Lu Lu
2022	J	`jnl`	Systems Biology: Identifiability analysis and parameter identification via systems-biology informed neural networks. CoRR Mitchell Daneker, Zhen Zhang, George Em Karniadakis, Lu Lu
2021	J	`jnl`	Convergence rate of DeepONets for learning operators arising from advection-diffusion equations. CoRR Beichuan Deng, Yeonjong Shin, Lu Lu, Zhongqiang Zhang, George Em Karniadakis
2021	J	`jnl`	Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients. npj Digit. Medicine Yixiang Deng, Lu Lu, Laura Aponte, Angeliki M. Angelidi, Vera Novak, George Em Karniadakis, Christos S. Mantzoros
2021	J	`jnl`	DeepM&Mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators. J. Comput. Phys. Zhiping Mao, Lu Lu, Olaf Marxen, Tamer A. Zaki, George Em Karniadakis
2021	J	`jnl`	DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks. J. Comput. Phys. Shengze Cai, Zhicheng Wang, Lu Lu, Tamer A. Zaki, George Em Karniadakis
2021	J	`jnl`	DeepXDE: A Deep Learning Library for Solving Differential Equations. SIAM Rev. Lu Lu, Xuhui Meng, Zhiping Mao, George Em Karniadakis
2021	J	`jnl`	Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems. CoRR Jeremy Yu, Lu Lu, Xuhui Meng, George Em Karniadakis
2021	J	`jnl`	How the spleen reshapes and retains young and old red blood cells: A computational investigation. PLoS Comput. Biol. He Li, Zixiang Leonardo Liu, Lu Lu, Pierre Buffet, George Em Karniadakis
2021	J	`jnl`	Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nat. Mach. Intell. Lu Lu, Pengzhan Jin, Guofei Pang, Zhongqiang Zhang, George Em Karniadakis
2021	J	`jnl`	Physics-Informed Neural Networks with Hard Constraints for Inverse Design. SIAM J. Sci. Comput. Lu Lu, Raphaël Pestourie, Wenjie Yao, Zhicheng Wang, Francesc Verdugo, Steven G. Johnson
2021	J	`jnl`	Physics-informed neural networks with hard constraints for inverse design. CoRR Lu Lu, Raphaël Pestourie, Wenjie Yao, Zhicheng Wang, Francesc Verdugo, Steven G. Johnson
2020	—	`conf`	DeepXDE: A Deep Learning Library for Solving Differential Equations. AAAI Spring Symposium: MLPS Lu Lu, Xuhui Meng, Zhiping Mao, George Em Karniadakis
2020	J	`jnl`	Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness. Neural Networks Pengzhan Jin, Lu Lu, Yifa Tang, George Em Karniadakis
2020	J	`jnl`	Systems biology informed deep learning for inferring parameters and hidden dynamics. PLoS Comput. Biol. Alireza Yazdani, Lu Lu, Maziar Raissi, George Em Karniadakis
2019	J	`jnl`	DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators. CoRR Lu Lu, Pengzhan Jin, George Em Karniadakis
2019	J	`jnl`	DeepXDE: A deep learning library for solving differential equations. CoRR Lu Lu, Xuhui Meng, Zhiping Mao, George E. Karniadakis
2019	J	`jnl`	Dying ReLU and Initialization: Theory and Numerical Examples. CoRR Lu Lu, Yeonjong Shin, Yanhui Su, George E. Karniadakis
2019	J	`jnl`	Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness. CoRR Pengzhan Jin, Lu Lu, Yifa Tang, George E. Karniadakis
2019	J	`jnl`	Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems. J. Comput. Phys. Dongkun Zhang, Lu Lu, Ling Guo, George Em Karniadakis
2019	J	`jnl`	fPINNs: Fractional Physics-Informed Neural Networks. SIAM J. Sci. Comput. Guofei Pang, Lu Lu, George Em Karniadakis
2018	J	`jnl`	Collapse of Deep and Narrow Neural Nets. CoRR Lu Lu, Yanhui Su, George E. Karniadakis
2017	J	`jnl`	OpenRBC: A Fast Simulator of Red Blood Cells at Protein Resolution. CoRR Yu-Hang Tang, Lu Lu, He Li, Constantinos Evangelinos, Leopold Grinberg, Vipin Sachdeva, George E. Karniadakis