Artificial intelligence (AI) increasingly supports subsurface characterization by integrating heterogeneous geophysical, geochemical, and spatial data. However, existing studies remain fragmented across disciplines and often leave unresolved questions about cross-modal physical coupling, fusion design, uncertainty prop...
H. Namazi, M. F. Sulaima, Ondrej Krejcar et al.· Environmental Earth Sciences· 0 citations
Deep neural networks often suffer significant accuracy degradation when exposed to real-world image corruptions and distribution shifts. To overcome the limitations of fixed, input-agnostic test-time augmentation (TTA), an adaptive framework is proposed that learns per-sample transformations via reinforcement learning....
T. Mittal, A. Dubey, Dharmender Saini et al.· Scientific Reports· 0 citations
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