Yue Yu
Orcid: 0000-0002-9150-3986Affiliations:
- Lehigh University, Department of Department of Mathematics, Bethlehem, PA, USA
- Brown University, Division of Applied Mathematics, Providence, RI, USA
According to our database1,
Yue Yu
authored at least 48 papers
between 2013 and 2025.
Collaborative distances:
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Bibliography
2025
Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery.
CoRR, May, 2025
Monotone Peridynamic Neural Operator for Nonlinear Material Modeling with Conditionally Unique Solutions.
CoRR, May, 2025
Nat. Comput. Sci., March, 2025
2024
Embedded Nonlocal Operator Regression (ENOR): Quantifying model error in learning nonlocal operators.
CoRR, 2024
CoRR, 2024
Large language models, physics-based modeling, experimental measurements: the trinity of data-scarce learning of polymer properties.
CoRR, 2024
CoRR, 2024
Heterogeneous Peridynamic Neural Operators: Discover Biotissue Constitutive Law and Microstructure From Digital Image Correlation Measurements.
CoRR, 2024
Peridynamic Neural Operators: A Data-Driven Nonlocal Constitutive Model for Complex Material Responses.
CoRR, 2024
Nonlocal Attention Operator: Materializing Hidden Knowledge Towards Interpretable Physics Discovery.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
Proceedings of the Pattern Recognition - 27th International Conference, 2024
Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws.
Proceedings of the Forty-first International Conference on Machine Learning, 2024
Integrating Markov Blanket Discovery Into Causal Representation Learning for Domain Generalization.
Proceedings of the Computer Vision - ECCV 2024, 2024
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024
2023
Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws.
CoRR, 2023
Towards a unified nonlocal, peridynamics framework for the coarse-graining of molecular dynamics data with fractures.
CoRR, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
INO: Invariant Neural Operators for Learning Complex Physical Systems with Momentum Conservation.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023
2022
Nonlocal kernel network (NKN): A stable and resolution-independent deep neural network.
J. Comput. Phys., 2022
An asymptotically compatible probabilistic collocation method for randomly heterogeneous nonlocal problems.
J. Comput. Phys., 2022
OBMeshfree: An optimization-based meshfree solver for nonlocal diffusion and peridynamics models.
CoRR, 2022
Bayesian Nonlocal Operator Regression (BNOR): A Data-Driven Learning Framework of Nonlocal Models with Uncertainty Quantification.
CoRR, 2022
MetaNOR: A Meta-Learnt Nonlocal Operator Regression Approach for Metamaterial Modeling.
CoRR, 2022
A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues from Digital Image Correlation Measurements.
CoRR, 2022
Learning Deep Implicit Fourier Neural Operators (IFNOs) with Applications to Heterogeneous Material Modeling.
CoRR, 2022
Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problems.
CoRR, 2022
A Meshfree Peridynamic Model for Brittle Fracture in Randomly Heterogeneous Materials.
CoRR, 2022
Proceedings of the International Conference on Machine Learning, 2022
2021
A physics-informed variational DeepONet for predicting the crack path in brittle materials.
CoRR, 2021
CoRR, 2021
An asymptotically compatible treatment of traction loading in linearly elastic peridynamic fracture.
CoRR, 2021
Proceedings of the 38th International Conference on Machine Learning, 2021
Data-driven Learning of Nonlocal Models: from high-fidelity simulations to constitutive laws.
Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to, 2021
2020
A Physically Consistent, Flexible, and Efficient Strategy to Convert Local Boundary Conditions into Nonlocal Volume Constraints.
SIAM J. Sci. Comput., 2020
CoRR, 2020
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020
2019
An Asymptotically Compatible Formulation for Local-to-Nonlocal Coupling Problems without Overlapping Regions.
CoRR, 2019
An Asymptotically Compatible Approach For Neumann-Type Boundary Condition On Nonlocal Problems.
CoRR, 2019
Proceedings of the 36th International Conference on Machine Learning, 2019
2016
Visualizing multiphysics, fluid-structure interaction phenomena in intracranial aneurysms.
Parallel Comput., 2016
J. Comput. Phys., 2016
2013
Generalized fictitious methods for fluid-structure interactions: Analysis and simulations.
J. Comput. Phys., 2013