| Rethinking the importance of sampling in physics-informed neural networks A Daw, J Bu, S Wang, P Perdikaris, A Karpatne arXiv preprint arXiv:2207.02338, 2022 | 68 | 2022 |
| Phynet: Physics guided neural networks for particle drag force prediction in assembly N Muralidhar, J Bu, Z Cao, L He, N Ramakrishnan, D Tafti, A Karpatne Proceedings of the 2020 SIAM international conference on data mining, 559-567, 2020 | 60 | 2020 |
| Quadratic residual networks: A new class of neural networks for solving forward and inverse problems in physics involving pdes J Bu, A Karpatne Proceedings of the 2021 SIAM International Conference on Data Mining (SDM …, 2021 | 44 | 2021 |
| CoPhy-PGNN: Learning Physics-guided Neural Networks with Competing Loss Functions for Solving Eigenvalue Problems M Elhamod, J Bu, C Singh, M Redell, A Ghosh, V Podolskiy, WC Lee, ... ACM Transactions on Intelligent Systems and Technology 13 (6), 1-23, 2022 | 40 | 2022 |
| Physics-guided deep learning for drag force prediction in dense fluid-particulate systems N Muralidhar, J Bu, Z Cao, L He, N Ramakrishnan, D Tafti, A Karpatne Big Data 8 (5), 431-449, 2020 | 36 | 2020 |
| Mitigating propagation failures in physics-informed neural networks using retain-resample-release (r3) sampling A Daw, J Bu, S Wang, P Perdikaris, A Karpatne arXiv preprint arXiv:2207.02338, 2022 | 24 | 2022 |
| Physics-guided design and learning of neural networks for predicting drag force on particle suspensions in moving fluids N Muralidhar, J Bu, Z Cao, L He, N Ramakrishnan, D Tafti, A Karpatne arXiv preprint arXiv:1911.04240, 2019 | 24 | 2019 |
| Mitigating propagation failures in pinns using evolutionary sampling A Daw, J Bu, S Wang, P Perdikaris, A Karpatne | 17 | 2022 |
| Learning compact representations of neural networks using discriminative masking (DAM) J Bu, A Daw, M Maruf, A Karpatne Advances in Neural Information Processing Systems 34, 3491-3503, 2021 | 5 | 2021 |
| Physics‐Informed Machine Learning for Optical Modes in Composites A Ghosh, M Elhamod, J Bu, WC Lee, A Karpatne, VA Podolskiy Advanced Photonics Research 3 (11), 2200073, 2022 | 4 | 2022 |
| Phyflow: Physics-guided deep learning for generating interpretable 3D flow fields N Muralidhar, J Bu, Z Cao, N Raj, N Ramakrishnan, D Tafti, A Karpatne 2021 IEEE International Conference on Data Mining (ICDM), 1246-1251, 2021 | 3 | 2021 |
| Let There Be Order: Rethinking Ordering in Autoregressive Graph Generation J Bu, KS Mehrab, A Karpatne arXiv preprint arXiv:2305.15562, 2023 | 1 | 2023 |
| Achieving More with Less: Learning Generalizable Neural Networks With Less Labeled Data and Computational Overheads J Bu Virginia Tech, 2023 | 1 | 2023 |
| Learning faster and better: embedding Maxwell equations into machine learning S Lynch, A Ghosh, J LaMountain, VA Podolskiy, J Bu, M Elhamod, ... Metamaterials XIV, PC1299009, 2024 | | 2024 |
| Beyond Discriminative Regions: Saliency Maps as Alternatives to CAMs for Weakly Supervised Semantic Segmentation M Maruf, A Daw, A Dutta, J Bu, A Karpatne arXiv preprint arXiv:2308.11052, 2023 | | 2023 |
| Science-informed machine-learning for optical composites (Conference Presentation) VA Podolskiy, A Ghosh, M Elhamod, J Bu, WC Lee, A Karpatne Metamaterials, Metadevices, and Metasystems 2022, PC121950Y, 2022 | | 2022 |
| Science-Guided Design and Evaluation of Machine Learning Models: A Case-Study on Multi-Phase Flows N Muralidhar, J Bu, Z Cao, L He, N Ramakrishnan, D Tafti, A Karpatne Knowledge Guided Machine Learning, 211-232, 2022 | | 2022 |
| Physics-guided machine learning for Maxwell's equations A Ghosh, M Elhamod, J Bu, WC Lee, A Karpatne, VA Podolskiy Metamaterials, Metadevices, and Metasystems 2021 11795, 117952H, 2021 | | 2021 |
| Beyond Observed Connections: Link Injection J Bu, M Maruf, A Daw arXiv preprint arXiv:2009.04447, 2020 | | 2020 |
| Unlocking quantum critical phenomena with physics guided artificial intelligence C Singh, M Redell, M Elhamod, J Bu, WC Lee, A Karpatne Bulletin of the American Physical Society 65, 2020 | | 2020 |
Anuj KarpatneVirginia Tech在 vt.edu 的电子邮件经过验证
