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näkemys - Physics, Machine Learning
Ensemble Learning for Physics Informed Neural Networks: A Gradient Boosting Approach
Gradient boosting enhances physics-informed neural networks' performance by employing a sequence of neural networks.
Physics-Informed Neural Network for Slow Invariant Manifolds in Stiff ODEs
Discovering slow invariant manifolds using a physics-informed neural network approach.
Integration of Physics-Derived Memristor Models with Machine Learning Frameworks
This work integrates physics-derived memristor models into machine learning frameworks to address device nonidealities and improve neural network accuracy. The main thesis is to bring physical dynamics into consideration while modeling nonidealities in memristive devices to guide the development of future integrated devices.
Storm Surge Modeling in the AI Era: Enhancing Forecasting Accuracy with LSTM-Based Machine Learning
The author proposes using an LSTM-based ML model to predict systemic errors in storm surge forecasting models, leading to improved accuracy by correcting biases.
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