User contributions for Cfmeaney
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15 November 2020
- 18:0618:06, 15 November 2020 diff hist +109 a fair comparison of graph neural networks for graph classification →References
- 17:4917:49, 15 November 2020 diff hist −1 orthogonal gradient descent for continual learning →Previous Work
- 17:4917:49, 15 November 2020 diff hist +213 orthogonal gradient descent for continual learning →References
- 17:4817:48, 15 November 2020 diff hist +2 orthogonal gradient descent for continual learning →Previous Work
- 17:4817:48, 15 November 2020 diff hist +208 orthogonal gradient descent for continual learning →Previous Work
- 17:1217:12, 15 November 2020 diff hist +217 stat940F21 →Paper presentation
- 17:0617:06, 15 November 2020 diff hist +7 stat940F21 →Paper presentation
14 November 2020
- 16:4016:40, 14 November 2020 diff hist +57 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Conclusion
- 16:1916:19, 14 November 2020 diff hist +43 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →References
- 16:1916:19, 14 November 2020 diff hist +141 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Conclusion
- 15:5815:58, 14 November 2020 diff hist +1 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Problem Setup
- 12:5312:53, 14 November 2020 diff hist +58 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →References
- 12:5312:53, 14 November 2020 diff hist +135 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Conclusion
- 12:4912:49, 14 November 2020 diff hist +509 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Navier-Stokes with Pressure
- 12:4412:44, 14 November 2020 diff hist +5 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Discrete-Time Example
- 12:4312:43, 14 November 2020 diff hist +28 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Continuous-Time Example
- 12:3912:39, 14 November 2020 diff hist +26 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Examples
- 12:3812:38, 14 November 2020 diff hist +15 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Continuous-Time Example
- 12:3112:31, 14 November 2020 diff hist +29 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Examples
- 12:1812:18, 14 November 2020 diff hist +642 Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations →Data-Driven Discovery of PDEs