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National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), China, College of Geosciences, China University of Petroleum (Beijing), China
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Surprisingly, deep ensembles *worsen* performance under physics constraints in PINNs for methane sorption, highlighting the importance of uncertainty quantification method selection in physics-informed machine learning.
Classical models of hydrogen storage in geological formations fall apart when applied to diverse samples, but this physics-informed neural network nails it, achieving R2 = 0.9544.