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Enabling Clean Energy Resilience with Machine Learning-Empowered Underground Hydrogen Storage
To address the urgent challenge of climate change, there is a critical need to transition away from fossil fuels towards sustainable energy systems, with renewable energy sources playing a pivotal role.
A Critical Review of Physics-Informed Machine Learning Applications in Subsurface Energy Systems
However, machine and deep learning models lack interpretability and limited domain-specific knowledge, especially in applications such as physics and engineering.
Predictive Scale-Bridging Simulations through Active Learning
Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements.
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