| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 2292 | 2018 |
| Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators J Pathak, S Subramanian, P Harrington, S Raja, A Chattopadhyay, ... arXiv preprint arXiv:2202.11214, 2022 | 722 | 2022 |
| Fourcastnet: Accelerating global high-resolution weather forecasting using adaptive fourier neural operators T Kurth, S Subramanian, P Harrington, J Pathak, M Mardani, D Hall, ... Proceedings of the platform for advanced scientific computing conference, 1-11, 2023 | 159 | 2023 |
| Towards foundation models for scientific machine learning: Characterizing scaling and transfer behavior S Subramanian, P Harrington, K Keutzer, W Bhimji, D Morozov, ... Advances in Neural Information Processing Systems 36, 71242-71262, 2023 | 71 | 2023 |
| A novel domain adaptation framework for medical image segmentation A Gholami, S Subramanian, V Shenoy, N Himthani, X Yue, S Zhao, P Jin, ... Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2019 | 71 | 2019 |
| Integrated biophysical modeling and image analysis: application to neuro-oncology A Mang, S Bakas, S Subramanian, C Davatzikos, G Biros Annual review of biomedical engineering 22 (1), 309-341, 2020 | 58 | 2020 |
| Simulation of glioblastoma growth using a 3D multispecies tumor model with mass effect S Subramanian, A Gholami, G Biros Journal of mathematical biology 79, 941-967, 2019 | 50 | 2019 |
| Where did the tumor start? An inverse solver with sparse localization for tumor growth models S Subramanian, K Scheufele, M Mehl, G Biros Inverse problems 36 (4), 045006, 2020 | 48 | 2020 |
| Adaptive self-supervision algorithms for physics-informed neural networks S Subramanian, RM Kirby, MW Mahoney, A Gholami ECAI 2023, 2234-2241, 2023 | 40 | 2023 |
| Quantitative in vivo imaging to enable tumour forecasting and treatment optimization G Lorenzo, DA Hormuth II, AM Jarrett, EABF Lima, S Subramanian, ... Cancer, complexity, computation, 55-97, 2022 | 35 | 2022 |
| Fully automatic calibration of tumor-growth models using a single mpMRI scan K Scheufele, S Subramanian, G Biros IEEE transactions on medical imaging 40 (1), 193-204, 2020 | 31 | 2020 |
| Multiatlas calibration of biophysical brain tumor growth models with mass effect S Subramanian, K Scheufele, N Himthani, G Biros Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 27 | 2020 |
| Ensemble inversion for brain tumor growth models with mass effect S Subramanian, A Ghafouri, KM Scheufele, N Himthani, C Davatzikos, ... IEEE transactions on medical imaging 42 (4), 982-995, 2022 | 22 | 2022 |
| Image-driven biophysical tumor growth model calibration K Scheufele, S Subramanian, A Mang, G Biros, M Mehl SIAM journal on scientific computing: a publication of the Society for …, 2020 | 21 | 2020 |
| Superbench: A super-resolution benchmark dataset for scientific machine learning P Ren, NB Erichson, S Subramanian, O San, Z Lukic, MW Mahoney arXiv preprint arXiv:2306.14070, 2023 | 16 | 2023 |
| Towards stability of autoregressive neural operators M McCabe, P Harrington, S Subramanian, J Brown arXiv preprint arXiv:2306.10619, 2023 | 13 | 2023 |
| SIBIA-GlS: Scalable biophysics-based image analysis for glioma segmentation A Mang, S Tharakan, A Gholami, N Nimthani, S Subramanian, J Levitt, ... Proc BraTS 2017 Workshop, 197-204, 2017 | 11 | 2017 |
| Estimating glioblastoma biophysical growth parameters using deep learning regression S Pati, V Sharma, H Aslam, SP Thakur, H Akbari, A Mang, S Subramanian, ... Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2021 | 9 | 2021 |
| Data-efficient operator learning via unsupervised pretraining and in-context learning W Chen, J Song, P Ren, S Subramanian, D Morozov, MW Mahoney Advances in Neural Information Processing Systems 37, 6213-6245, 2025 | 8 | 2025 |
| Analyzing and exploring training recipes for large-scale transformer-based weather prediction JD Willard, P Harrington, S Subramanian, A Mahesh, TA O'Brien, ... arXiv preprint arXiv:2404.19630, 2024 | 7 | 2024 |
Peter HarringtonNERSC, Lawrence Berkeley National Laboratoryכתובת אימייל מאומתת בדומיין lbl.gov