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Shandian Zhe
- > Home > Persons > Shandian Zhe
Publications
- 2023
- [j14]Michael Penwarden, Shandian Zhe, Akil Narayan, Robert M. Kirby:
A metalearning approach for Physics-Informed Neural Networks (PINNs): Application to parameterized PDEs. J. Comput. Phys. 477: 111912 (2023) - [j13]Michael Penwarden, Ameya D. Jagtap, Shandian Zhe, George Em Karniadakis, Robert M. Kirby:
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions. J. Comput. Phys. 493: 112464 (2023) - [c54]Shibo Li, Zheng Wang, Akil Narayan, Robert M. Kirby, Shandian Zhe:
Meta-Learning with Adjoint Methods. AISTATS 2023: 7239-7251 - [c51]Shibo Li, Michael Penwarden, Yiming Xu, Conor Tillinghast, Akil Narayan, Mike Kirby, Shandian Zhe:
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks. ICML 2023: 19855-19881 - [c50]Shikai Fang, Xin Yu, Shibo Li, Zheng Wang, Mike Kirby, Shandian Zhe:
Streaming Factor Trajectory Learning for Temporal Tensor Decomposition. NeurIPS 2023 - [i42]Hongsup Oh, Roman Amici, Geoffrey F. Bomarito, Shandian Zhe, Robert M. Kirby, Jacob D. Hochhalter:
Genetic Programming Based Symbolic Regression for Analytical Solutions to Differential Equations. CoRR abs/2302.03175 (2023) - [i41]Michael Penwarden, Ameya D. Jagtap, Shandian Zhe, George Em Karniadakis, Robert M. Kirby:
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions. CoRR abs/2302.14227 (2023) - [i38]Shibo Li, Xin Yu, Wei W. Xing, Mike Kirby, Akil Narayan, Shandian Zhe:
Multi-Resolution Active Learning of Fourier Neural Operators. CoRR abs/2309.16971 (2023) - [i37]Da Long, Wei W. Xing, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney:
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels. CoRR abs/2310.05387 (2023) - [i36]Shikai Fang, Xin Yu, Shibo Li, Zheng Wang, Robert M. Kirby, Shandian Zhe:
Streaming Factor Trajectory Learning for Temporal Tensor Decomposition. CoRR abs/2310.17021 (2023) - [i34]Shikai Fang, Madison Cooley, Da Long, Shibo Li, Robert M. Kirby, Shandian Zhe:
Solving High Frequency and Multi-Scale PDEs with Gaussian Processes. CoRR abs/2311.04465 (2023) - [i33]Shikai Fang, Xin Yu, Zheng Wang, Shibo Li, Mike Kirby, Shandian Zhe:
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data. CoRR abs/2311.04829 (2023) - 2022
- [j12]Michael Penwarden, Shandian Zhe, Akil Narayan, Robert M. Kirby:
Multifidelity modeling for Physics-Informed Neural Networks (PINNs). J. Comput. Phys. 451: 110844 (2022) - [c48]Zheng Wang, Wei W. Xing, Robert M. Kirby, Shandian Zhe:
Physics Informed Deep Kernel Learning. AISTATS 2022: 1206-1218 - [c47]Shibo Li, Zheng Wang, Robert M. Kirby, Shandian Zhe:
Deep Multi-Fidelity Active Learning of High-Dimensional Outputs. AISTATS 2022: 1694-1711 - [c46]Shikai Fang, Akil Narayan, Robert M. Kirby, Shandian Zhe:
Bayesian Continuous-Time Tucker Decomposition. ICML 2022: 6235-6245 - [c45]Shibo Li, Robert M. Kirby, Shandian Zhe:
Decomposing Temporal High-Order Interactions via Latent ODEs. ICML 2022: 12797-12812 - [c44]Da Long, Zheng Wang, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney:
AutoIP: A United Framework to Integrate Physics into Gaussian Processes. ICML 2022: 14210-14222 - [c38]Shibo Li, Zheng Wang, Robert M. Kirby, Shandian Zhe:
Infinite-Fidelity Coregionalization for Physical Simulation. NeurIPS 2022 - [c37]Shibo Li, Jeff M. Phillips, Xin Yu, Robert M. Kirby, Shandian Zhe:
Batch Multi-Fidelity Active Learning with Budget Constraints. NeurIPS 2022 - [i31]Vahid Keshavarzzadeh, Shandian Zhe, Robert M. Kirby, Akil Narayan:
GP-HMAT: Scalable, O(n log(n)) Gaussian Process Regression with Hierarchical Low-Rank Matrices. CoRR abs/2201.00888 (2022) - [i30]Da Long, Zheng Wang, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney:
AutoIP: A United Framework to Integrate Physics into Gaussian Processes. CoRR abs/2202.12316 (2022) - [i27]Shibo Li, Zheng Wang, Robert M. Kirby, Shandian Zhe:
Infinite-Fidelity Coregionalization for Physical Simulation. CoRR abs/2207.00678 (2022) - [i23]Shibo Li, Michael Penwarden, Robert M. Kirby, Shandian Zhe:
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks. CoRR abs/2210.12669 (2022) - [i22]Shibo Li, Jeff M. Phillips, Xin Yu, Robert M. Kirby, Shandian Zhe:
Batch Multi-Fidelity Active Learning with Budget Constraints. CoRR abs/2210.12704 (2022) - 2021
- [j11]Wei W. Xing, Robert M. Kirby, Shandian Zhe:
Deep coregionalization for the emulation of simulation-based spatial-temporal fields. J. Comput. Phys. 428: 109984 (2021) - [c36]Zheng Wang, Wei W. Xing, Robert Michael Kirby, Shandian Zhe:
Multi-Fidelity High-Order Gaussian Processes for Physical Simulation. AISTATS 2021: 847-855 - [c32]Shibo Li, Robert M. Kirby, Shandian Zhe:
Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks. NeurIPS 2021: 25463-25475 - [c31]Aditi S. Krishnapriyan, Amir Gholami, Shandian Zhe, Robert M. Kirby, Michael W. Mahoney:
Characterizing possible failure modes in physics-informed neural networks. NeurIPS 2021: 26548-26560 - [c30]Shikai Fang, Robert M. Kirby, Shandian Zhe:
Bayesian streaming sparse Tucker decomposition. UAI 2021: 558-567 - [i21]Wei W. Xing, Akeel A. Shah, Peng Wang, Shandian Zhe, Qian Fu, Robert M. Kirby:
Residual Gaussian Process: A Tractable Nonparametric Bayesian Emulator for Multi-fidelity Simulations. CoRR abs/2104.03743 (2021) - [i20]Shibo Li, Robert M. Kirby, Shandian Zhe:
Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks. CoRR abs/2106.09884 (2021) - [i19]Michael Penwarden, Shandian Zhe, Akil Narayan, Robert M. Kirby:
Multifidelity Modeling for Physics-Informed Neural Networks (PINNs). CoRR abs/2106.13361 (2021) - [i18]Aditi S. Krishnapriyan, Amir Gholami, Shandian Zhe, Robert M. Kirby, Michael W. Mahoney:
Characterizing possible failure modes in physics-informed neural networks. CoRR abs/2109.01050 (2021) - [i17]Shibo Li, Zheng Wang, Akil Narayan, Robert Michael Kirby, Shandian Zhe:
Meta-Learning with Adjoint Methods. CoRR abs/2110.08432 (2021) - [i15]Michael Penwarden, Shandian Zhe, Akil Narayan, Robert M. Kirby:
Physics-Informed Neural Networks (PINNs) for Parameterized PDEs: A Metalearning Approach. CoRR abs/2110.13361 (2021) - 2020
- [c29]Wei W. Xing, Shireen Y. Elhabian, Robert Michael Kirby, Ross T. Whitaker, Shandian Zhe:
Infinite ShapeOdds: Nonparametric Bayesian Models for Shape Representations. AAAI 2020: 6462-6469 - [c24]Shibo Li, Wei W. Xing, Robert M. Kirby, Shandian Zhe:
Scalable Gaussian Process Regression Networks. IJCAI 2020: 2456-2462 - [c22]Shibo Li, Wei W. Xing, Robert M. Kirby, Shandian Zhe:
Multi-Fidelity Bayesian Optimization via Deep Neural Networks. NeurIPS 2020 - [i13]Shibo Li, Wei W. Xing, Mike Kirby, Shandian Zhe:
Scalable Variational Gaussian Process Regression Networks. CoRR abs/2003.11489 (2020) - [i12]Zheng Wang, Wei W. Xing, Robert Michael Kirby, Shandian Zhe:
Multi-Fidelity High-Order Gaussian Processes for Physical Simulation. CoRR abs/2006.04972 (2020) - [i11]Zheng Wang, Wei W. Xing, Robert Michael Kirby, Shandian Zhe:
Physics Regularized Gaussian Processes. CoRR abs/2006.04976 (2020) - [i10]Shibo Li, Wei W. Xing, Mike Kirby, Shandian Zhe:
Multi-Fidelity Bayesian Optimization via Deep Neural Networks. CoRR abs/2007.03117 (2020) - [i7]Shibo Li, Robert M. Kirby, Shandian Zhe:
Deep Multi-Fidelity Active Learning of High-dimensional Outputs. CoRR abs/2012.00901 (2020) - 2019
- [c20]Shandian Zhe, Wei W. Xing, Robert M. Kirby:
Scalable High-Order Gaussian Process Regression. AISTATS 2019: 2611-2620 - [i6]Wei W. Xing, Robert M. Kirby, Shandian Zhe:
Deep Coregionalization for the Emulation of Spatial-Temporal Fields. CoRR abs/1910.07577 (2019)
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