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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...
南京农业大学农学院《Science of The Total Environment》发表智慧农业团队“Improving the spatial and temporal estimation of ecosystem respiration using multi-source data and machine learning methods in a rainfed winter wheat cropland”（图）
智慧农业 碳循环 生态系统
2023/10/28
2023年2月25日，农学院智慧农业团队在《Science of The Total Environment》期刊发表了题为“Improving the spatial and temporal estimation of ecosystem respiration using multi-source data and machine learning methods in a rainfed ...
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...
Fusion, which promises practically unlimited, carbon-free energy using the same processes that power the sun, is at the heart of a worldwide research effort that could help mitigate climate change.
Machine learning, harnessed to extreme computing, aids fusion energy development（图）
机器学习 极限计算 聚变能源开发
2023/6/21
MIT research scientists Pablo Rodriguez-Fernandez and Nathan Howard have just completed one of the most demanding calculations in fusion science — predicting the temperature and density profiles of a ...
Duchenne muscular dystrophy (DMD), a rare genetic disease usually diagnosed in young boys, gradually weakens muscles across the body until the heart or lungs fail. Symptoms often show up by age 5; as ...
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...
River flow: New machine learning methods could improve environmental predictions
river flow improve environmental predictions algorithm environmental prediction
2021/8/5
Machine learning algorithms do a lot for us every day -- send unwanted email to our spam folder, warn us if our car is about to back into something, and give us recommendations on what TV show to watc...
APPLICATION OF MACHINE LEARNING IN URBAN GREENERY LAND COVER EXTRACTION
Neural Network Machine Learning Greenery Land Cover Auto Extraction Multispectral Image
2018/5/15
Urban greenery is a critical part of the modern city and the greenery coverage information is essential for land resource management, environmental monitoring and urban planning. It is a challenging w...
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...
Machine learning could solve riddles of galaxy formation
Machine learning galaxy formation
2015/12/7
A new machine-learning simulation system developed at the University of Illinois promises cosmologists an expanded suite of galaxy models – a necessary first step to developing more accurate and relev...
A minimax theorem with applications to machine learning, signal processing, and finance
convex optimization minimax theorem robust optimization
2015/8/10
This paper concerns a fractional function of the form x^Ta/sqrt{x^TBx}, where B is positive definite. We consider the game of choosing x from a convex set, to maximize the function, and choosing (a,B)...
Niche Modeling Perspective on Geographic Range Predictions in the Marine Environment Using a Machine-learning Algorithm
Geographic Range Predictions Machine-learning Algorithm
2015/7/31
Biological communities are changing drastically in response to global climate change (Walther et al., 2002), changes in use by human populations (Krishtalka et al., 2002), and introduction of exotic s...
Using Machine Learning for Discovery in Synoptic Survey Imaging
Machine Learning Discovery Synoptic Survey Imaging
2012/11/23
Modern time-domain surveys continuously monitor large swaths of the sky to look for astronomical variability. Astrophysical discovery in such data sets is complicated by the fact that detections of re...
Fast supersymmetry phenomenology at the Large Hadron Collider using machine learning techniques
Hadron Collider using machine Fast supersymmetry phenomenology
2011/9/15
A pressing problem for supersymmetry (SUSY) phenomenologists is how to incorporate Large Hadron Collider search results into parameter fits designed to measure or constrain the SUSY parameters. Owing ...