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Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Machine learning methods for forward and inverse PDEs
正反向 偏微分方程 机器学习
2023/4/27
本报告介绍我们近期的两项工作。(1) 求解PDE的PINN方法在处理时间发展方程时往往遇到难以收敛的困难。我们发展了时间方向的预训练PINN方法及自适应步长方法，解决了收敛性困难，使PDE求解精度能够得到系统性提高。我们在一系列时间发展方程上获得了比文献报道更精确的训练结果。(2) 辐射调源问题是一个典型的反问题，要求调整辐射输运方程的边条件(源)，使解满足特定设计目标。我们针对辐射输运方程的时序...
Last year, MIT researchers announced that they had built “liquid” neural networks, inspired by the brains of small species: a class of flexible, robust machine learning models that learn on the job an...
Tiny machine learning design alleviates a bottleneck in memory usage on internet-of-things devices（图）
林肯实验室 图开发 研讨会 网络科学 虚假信息追踪 机器学习
2023/6/21
As the Covid-19 pandemic has shown, we live in a richly connected world, facilitating not only the efficient spread of a virus but also of information and influence. What can we learn by analyzing the...
2018集成机器学习和预测模拟研讨会（lntegrating Machine Learning and Predictive Simulatio）
2018 集成机器学习和预测模拟 研讨会
2017/12/20
Industrial design and engineering, as well as healthcare, are facing unprecedented challenges with product complexity and increasingly faster innovation cycles. On the one hand, advances in computer m...