随后，出现了许多基于 PINNs的改进的深度学习框架，以提高其鲁棒性以及用于其他领域的泛化能力。例如基于最...
PINNacle还提供了深入分析，以指导未来研究，特别是在领域分解方法和损失重新加权等方面，以处理多尺度问题...
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...
“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...
we fabricated a number of microfluidic devices based on the inferred geometries. Each of these devices was then injected with1 μm polystyrene particles suspended in fluid into the shaped channels, and a SAW was applied to the device. The r...
In this manuscript, we provide a structured and comprehensive overview of techniques to integrate machine learning with physics-based modeling. First, we provide a summary of application areas for which these approaches have been applied. Then, we describe classes of methodologies used to construct ph...
underlying nonlinear input-output relationship in complex systems. Unfortunately, dealing with such high dimensional-complex systems are not exempt from the curse of dimensionality, which Bellman first described in the context of optimal control problems [15]. However, machine learning-based algorithms ...
This collection will gather the latest advances in physics-informed machine learning applications in sciences and engineering for real world applications.
In Section 2 we describe the collection of data needed for training and applying the machine learning model. Section 3 presents the process of training artificial neural networks, and the selection of hyperparameters based on validation data. Section 4 shows the resulting model predictions on test ...
1. Maziar Raissi:Data-Efficient Deep Learning using Physics-Informed Neural Networks 2. Paris Perdikaris:Bridging Physical Models and Observational Data with Physics-Informed Deep Learning 3. George Karniadakis:DeepOnet: Learning nonlinear operators based on the universal approximation theorem of operators...