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NewPINNs: Physics-Informing Neural Networks Using Conventional Solvers for Partial Differential Equations
Satish Chandran, Maedeh Makki, Maziar Raissi +2
We introduce NewPINNs, a physics-informing learning framework that couples neural networks with conventional numerical solvers for solving differential equations. Rather than enfor…
PUNCH: Physics-informed Uncertainty-aware Network for Coronary Hemodynamics
Sukirt Thakur, Marcus Roper, Yang Zhou +8
More than 10 million coronary angiograms are performed globally each year, providing a gold standard for detecting obstructive coronary artery disease. Yet, no obstructive lesions…
Where Did Your Model Learn That? Label-free Influence for Self-supervised Learning
Nidhin Harilal, Amit Kiran Rege, Reza Akbarian Bafghi +2
Self-supervised learning (SSL) has revolutionized learning from large-scale unlabeled datasets, yet the intrinsic relationship between pretraining data and the learned representati…