Physics-guided neural networks (pgnn): An application in lake temperature modeling A Daw, A Karpatne, WD Watkins, JS Read, V Kumar Knowledge Guided Machine Learning, 353-372, 2022 | 222 | 2022 |
Physics-guided architecture (pga) of neural networks for quantifying uncertainty in lake temperature modeling A Daw, RQ Thomas, CC Carey, JS Read, AP Appling, A Karpatne Proceedings of the 2020 siam international conference on data mining, 532-540, 2020 | 139 | 2020 |
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 | 58 | 2022 |
PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics A Daw, M Maruf, A Karpatne Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 34 | 2021 |
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 | 16 | 2022 |
Mitigating propagation failures in pinns using evolutionary sampling A Daw, J Bu, S Wang, P Perdikaris, A Karpatne | 14 | 2022 |
Physics-guided neural networks (pgnn): An application in lake temperature modeling, arXiv A Daw, A Karpatne, W Watkins, J Read, V Kumar arXiv preprint arXiv:1710.11431, 2017 | 11 | 2017 |
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 | 4 | 2021 |
Physics-aware Architecture of Neural Networks for Uncertainty Quantification: Application in Lake Temperature Modeling A Daw, A Karpatne FEED Workshop at Knowledge Discovery and Data Mining Conference (SIGKDD …, 2019 | 4 | 2019 |
Multi-task Learning for Source Attribution and Field Reconstruction for Methane Monitoring A Daw, K Yeo, A Karpatne, L Klein 2022 IEEE International Conference on Big Data (Big Data), 4835-4841, 2022 | 3 | 2022 |
Modular Compositional Learning Improves 1D Hydrodynamic Lake Model Performance by Merging Process‐Based Modeling With Deep Learning R Ladwig, A Daw, EA Albright, C Buelo, A Karpatne, MF Meyer, A Neog, ... Journal of Advances in Modeling Earth Systems 16 (1), e2023MS003953, 2024 | 1 | 2024 |
Physics-guided architecture (PGA) of LSTM models for uncertainty quantification in lake temperature modeling A Daw, RQ Thomas, CC Carey, JS Read, AP Appling, A Karpatne Knowledge Guided Machine Learning, 399-416, 2022 | 1 | 2022 |
Physics-Informed Discriminator (PID) for Conditional Generative Adversarial Nets A Daw, M Maruf, A Karpatne Advances in Neural Information Processing Systems, Machine Learning and …, 2020 | 1 | 2020 |
Physics-informed Machine Learning with Uncertainty Quantification A Daw Virginia Tech, 2024 | | 2024 |
Motion Enhanced Multi‐Level Tracker (MEMTrack): A Deep Learning‐Based Approach to Microrobot Tracking in Dense and Low‐Contrast Environments M Sawhney, B Karmarkar, EJ Leaman, A Daw, A Karpatne, B Behkam Advanced Intelligent Systems, 2300590, 2024 | | 2024 |
MEMTRACK: A Deep Learning-Based Approach to Microrobot Tracking in Dense and Low-Contrast Environments M Sawhney, B Karmarkar, EJ Leaman, A Daw, A Karpatne, B Behkam arXiv preprint arXiv:2310.09441, 2023 | | 2023 |
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 |
Source Identification and Field Reconstruction of Advection-Diffusion Process from Sparse Sensor Measurements A Daw, K Yeo, A Karpatne, L Klein Advances in Neural Information Processing Systems, Machine Learning and …, 2022 | | 2022 |
Deep Learning Enabled Label-free Cell Force Computation in Deformable Fibrous Environments A Padhi, A Daw, M Sawhney, MM Talukder, A Agashe, S Kale, A Karpatne, ... https://www.biorxiv.org/content/10.1101/2022.10.24.513423v1, 2022 | | 2022 |
Beyond Observed Connections: Link Injection J Bu, M Maruf, A Daw arXiv preprint arXiv:2009.04447, 2020 | | 2020 |