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physics.comp-ph2026
Physics-informed Kolmogorov-Arnold networks (PIKANs) for noise-robust reconstruction of boiling heat flux from infrared measurements
Siavash Khodakarami, Parimala Vardhan Vydyula, Aniruddha Bora +2
Recovering a noise-amplified inverse quantity from corrupted boundary data is a recurring problem in physics-informed learning for real-world applications. Specifically, small pert…
physics.comp-ph2026
Multi-fidelity surrogates for mechanics of composites: from co-kriging to multi-fidelity neural networks
Haizhou Wen, Elham Kiyani, Gang Li +3
Composite materials exhibit strongly hierarchical and anisotropic properties governed by coupled mechanisms spanning constituents, plies, laminates, structures, and manufacturing h…