有兴趣的同学可以看下Score-Based Generative Modeling through Stochastic Differential Equations. 后期提出...
“We used machine learning to predict the time-series-based mutation rate of COVID-19, and then incorporated that as an independent parameter into the prediction of pandemic dynamics to see if it could help us better predict the trend of the COVID-19 pandemic,” says Hu. Hu, who had pre...
Structure-based drug design and virtual screening have become common approaches for drug discovery. The predictive performance of scoring functions is essential for such methodologies1,2,3. However, accurate prediction of protein–ligand binding affinity remains a major challenge for current scoring functi...
This collection will gather the latest advances in physics-informed machine learning applications in sciences and engineering for real world applications.
Sam Raymond is a postdoctoral scholar at Stanford University, having completed his Ph.D. in the Center for Computational Science and Engineering (CCSE) at MIT. His research interests include physics-informed machine learning, applying high-perform...
github 上面整理的一个 repo：https://github.com/csjiezhao/Physics-Based-Deep-Learning
PhysicsNeMo 22.03,the cutting-edge framework for developing physics-based machine learning models, offers developers key capabilities such as novel physics informed and data-driven AI architectures, and integration into theOmniverse(OV) platform.
It applies adaptive function, integrating heat transfer law and boundary condition equations to loss function, which has fast convergence and strong physics-based constraints. The reality-augmented data are acquired by few experiments and an accurate 3D heat transfer model covering turbulence. The ...
Physics-informed machine learning integrates seamlessly data and mathematical physics models, even in partially understood, uncertain and high-dimensional contexts. Kernel-based or neural network-based regression methods offer effective, simple and meshless implementations. Physics-informed neural networks are ...
MadMiner: machine learning-based inference for particle physics. Comput. Softw. Big Sci. 4, 3 (2020). Article Google Scholar Brehmer, J., Louppe, G., Pavez, J. & Cranmer, K. Mining gold from implicit models to improve likelihood-free inference. Proc. Natl Acad. Sci. USA 117, ...