1.0Microsoft Researchhttps://www.microsoft.com/en-us/researchBin Shaohttps://www.microsoft.com/en-us/research/people/binshao/Fast Neural PDE Solver - Microsoft Researchrich600338<blockquote class="wp-embedded-content" data-secret="bjNyziVJVe"><a href="https://www.microsoft.com/en-us/research/project/lordnet-neural-pde-solver/">Fast Neural PDE Solver</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/project/lordnet-neural-pde-solver/embed/#?secret=bjNyziVJVe" width="600" height="338" title="“Fast Neural PDE Solver” — Microsoft Research" data-secret="bjNyziVJVe" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script type="text/javascript">
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Understanding many sustainability issues relates fundamentally to PDEs, from macroscope to microscope, such as navier stokes equation and Schrödinger equations. Solving these PDEs enables us to understand and forecast the world and is a critical task in our pursue of sustainability. However, the traditional numerical solvers are slow and we have to trade off between the resolution or the accuracy and the speed. We propose a new learning method that can better leverage Physics behind those PDEs.