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SOME APPROXIMATE METHODS FOR SOLVING NONLINEAR ILL-POSED PROBLEMS
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Deep learning-based artefact reduction in low-dose dental cone beam computed tomography with high-attenuation materials. [PDF]
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Sensing Through Tissues Using Diffuse Optical Imaging and Genetic Programming. [PDF]
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WF-PINNs: solving forward and inverse problems of burgers equation with steep gradients using weak-form physics-informed neural networks. [PDF]
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Data-driven material modeling based on the Constitutive Relation Error. [PDF]
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Aggregation in Ill-Conditioned Regression Models: A Comparison with Entropy-Based Methods. [PDF]
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