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Jie Bu
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2020 – today
- 2023
- [b1]Jie Bu:
Achieving More with Less: Learning Generalizable Neural Networks With Less Labeled Data and Computational Overheads. Virginia Tech, Blacksburg, VA, USA, 2023 - [c6]Arka Daw, Jie Bu, Sifan Wang, Paris Perdikaris, Anuj Karpatne:
Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling. ICML 2023: 7264-7302 - [i8]Jie Bu, Kazi Sajeed Mehrab, Anuj Karpatne:
Let There Be Order: Rethinking Ordering in Autoregressive Graph Generation. CoRR abs/2305.15562 (2023) - [i7]M. Maruf, Arka Daw, Amartya Dutta, Jie Bu, Anuj Karpatne:
Beyond Discriminative Regions: Saliency Maps as Alternatives to CAMs for Weakly Supervised Semantic Segmentation. CoRR abs/2308.11052 (2023) - 2022
- [j3]Nan Zhao
, Jie Bu:
Robust multi-view subspace clustering based on consensus representation and orthogonal diversity. Neural Networks 150: 102-111 (2022) - [j2]Mohannad Elhamod
, Jie Bu
, Christopher Singh
, Matthew Redell
, Abantika Ghosh
, Viktor Podolskiy
, Wei-Cheng Lee
, Anuj Karpatne
:
CoPhy-PGNN: Learning Physics-guided Neural Networks with Competing Loss Functions for Solving Eigenvalue Problems. ACM Trans. Intell. Syst. Technol. 13(6): 92:1-92:23 (2022) - [i6]Arka Daw, Jie Bu, Sifan Wang, Paris Perdikaris, Anuj Karpatne:
Rethinking the Importance of Sampling in Physics-informed Neural Networks. CoRR abs/2207.02338 (2022) - 2021
- [c5]Mohannad Elhamod, Jie Bu, Christopher Singh, Matthew Redell, Abantika Ghosh, Viktor Podolskiy, Wei-Cheng Lee, Anuj Karpatne:
Learning Physics-guided Neural Networks with Competing Physics Loss: A Summary of Results in Solving Eigenvalue Problems. AAAI Spring Symposium: MLPS 2021 - [c4]Nikhil Muralidhar, Jie Bu, Ze Cao, Neil Raj, Naren Ramakrishnan
, Danesh K. Tafti, Anuj Karpatne:
PhyFlow: Physics-Guided Deep Learning for Generating Interpretable 3D Flow Fields. ICDM 2021: 1246-1251 - [c3]Jie Bu, Arka Daw, M. Maruf, Anuj Karpatne:
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM). NeurIPS 2021: 3491-3503 - [c2]Jie Bu, Anuj Karpatne:
Quadratic Residual Networks: A New Class of Neural Networks for Solving Forward and Inverse Problems in Physics Involving PDEs. SDM 2021: 675-683 - [i5]Jie Bu, Anuj Karpatne:
Quadratic Residual Networks: A New Class of Neural Networks for Solving Forward and Inverse Problems in Physics Involving PDEs. CoRR abs/2101.08366 (2021) - [i4]Jie Bu, Arka Daw, M. Maruf, Anuj Karpatne:
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM). CoRR abs/2110.00684 (2021) - 2020
- [j1]Nikhil Muralidhar, Jie Bu, Ze Cao, Long He, Naren Ramakrishnan
, Danesh K. Tafti, Anuj Karpatne:
Physics-Guided Deep Learning for Drag Force Prediction in Dense Fluid-Particulate Systems. Big Data 8(5): 431-449 (2020) - [c1]Nikhil Muralidhar, Jie Bu, Ze Cao, Long He, Naren Ramakrishnan
, Danesh K. Tafti, Anuj Karpatne:
PhyNet: Physics Guided Neural Networks for Particle Drag Force Prediction in Assembly. SDM 2020: 559-567 - [i3]Jie Bu, Mohannad Elhamod, Christopher Singh, Matthew Redell, Wei-Cheng Lee, Anuj Karpatne:
Learning Neural Networks with Competing Physics Objectives: An Application in Quantum Mechanics. CoRR abs/2007.01420 (2020) - [i2]Jie Bu, M. Maruf, Arka Daw:
Beyond Observed Connections : Link Injection. CoRR abs/2009.04447 (2020)
2010 – 2019
- 2019
- [i1]Nikhil Muralidhar, Jie Bu, Ze Cao, Long He, Naren Ramakrishnan, Danesh K. Tafti, Anuj Karpatne:
Physics-guided Design and Learning of Neural Networks for Predicting Drag Force on Particle Suspensions in Moving Fluids. CoRR abs/1911.04240 (2019)
Coauthor Index
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