| Local implicit grid representations for 3d scenes C Jiang, A Sud, A Makadia, J Huang, M Nießner, T Funkhouser Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 211 | 2020 |
| Spherical CNNs on Unstructured Grids C Jiang, J Huang, K Kashinath, P Marcus, M Nießner International Conference on Learning Representation (2019), 2019 | 111 | 2019 |
| Physics-informed machine learning: case studies for weather and climate modelling K Kashinath, M Mustafa, A Albert, JL Wu, C Jiang, S Esmaeilzadeh, ... Philosophical Transactions of the Royal Society A 379 (2194), 20200093, 2021 | 83 | 2021 |
| Meshfreeflownet: A physics-constrained deep continuous space-time super-resolution framework S Esmaeilzadeh, K Azizzadenesheli, K Kashinath, M Mustafa, ... SC20: International Conference for High Performance Computing, Networking …, 2020 | 40 | 2020 |
| Shapeflow: Learnable deformation flows among 3d shapes C Jiang, J Huang, A Tagliasacchi, LJ Guibas Advances in Neural Information Processing Systems 33, 9745-9757, 2020 | 37 | 2020 |
| Shape as points: A differentiable poisson solver S Peng, C Jiang, Y Liao, M Niemeyer, M Pollefeys, A Geiger Advances in Neural Information Processing Systems 34, 13032-13044, 2021 | 23 | 2021 |
| Adversarial texture optimization from rgb-d scans J Huang, J Thies, A Dai, A Kundu, C Jiang, LJ Guibas, M Nießner, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 23 | 2020 |
| Enforcing physical constraints in cnns through differentiable pde layer K Kashinath, P Marcus ICLR 2020 Workshop on Integration of Deep Neural Models and Differential …, 2020 | 15 | 2020 |
| Hierarchical detail enhancing mesh-based shape generation with 3d generative adversarial network C Jiang, P Marcus arXiv preprint arXiv:1709.07581, 2017 | 13 | 2017 |
| Finding the optimal shape of the leading-and-trailing car of a high-speed train using design-by-morphing S Oh, CH Jiang, C Jiang, PS Marcus Computational Mechanics 62 (1), 23-45, 2018 | 11 | 2018 |
| Leveraging Bayesian analysis to improve accuracy of approximate models B Nadiga, C Jiang, D Livescu Journal of Computational Physics 394, 280-297, 2019 | 10 | 2019 |
| Convolutional Neural Networks on non-uniform geometrical signals using Euclidean spectral transformation C Jiang, D Wang, J Huang, P Marcus, M Nießner International Conference on Learning Representation (2019), 2019 | 8 | 2019 |
| Local implicit grid representations for 3d scenes. In 2020 IEEE C Jiang, A Sud, A Makadia, J Huang, T Funkhouser CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2, 2020 | 7 | 2020 |
| Meshode: A robust and scalable framework for mesh deformation J Huang, CM Jiang, B Leng, B Wang, L Guibas arXiv preprint arXiv:2005.11617, 2020 | 6 | 2020 |
| Ddsl: Deep differentiable simplex layer for learning geometric signals C Jiang, D Lansigan, P Marcus, M Nießner Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 4 | 2019 |
| Neural Network Optimization Under Partial Differential Equation Constraints K Kashinath, C Jiang, GE Jergensen, M Prabhat, P Marcus, ... APS Division of Fluid Dynamics Meeting Abstracts, C17. 008, 2019 | | 2019 |
| Towards Leveraging Machine Learning and Other Statistical Methods To Improve Turbulence Modeling B Nadiga, C Jiang, D Livescu Bulletin of the American Physical Society 63, 2018 | | 2018 |
| Bridging simulation and deep learning-convolutional neural networks on unstructured grids C Jiang, K Kashinath, P Marcus, M Prabhat Bulletin of the American Physical Society 63, 2018 | | 2018 |
| Drag Reduction of an Airfoil Using Deep Learning C Jiang, A Sun, P Marcus APS Division of Fluid Dynamics Meeting Abstracts, D31. 008, 2017 | | 2017 |
| Shape Optimization of A Turbine-99 Draft Tube Using Design-by-Morphing S Oh, CH Jiang, P Marcus, D Gutzwiller, A Demeulenaere, C Jiang APS Division of Fluid Dynamics Meeting Abstracts, L4. 004, 2016 | | 2016 |
Philip MarcusProfessor of Mechanical Engineering, University of California at BerkeleyVerified email at me.berkeley.edu
