40 citations · 61 across the 12 of their papers we have counts for
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cs.LG2024★ 40 cited
Challenges in Training PINNs: A Loss Landscape Perspective
Pratik Rathore, Weimu Lei, Zachary Frangella +2
This paper explores challenges in training Physics-Informed Neural Networks (PINNs), emphasizing the role of the loss landscape in the training process. We examine difficulties in…
cs.LG2024
Speeding up and reducing memory usage for scientific machine learning via mixed precision
Joel Hayford, Jacob Goldman-Wetzler, Eric Wang +1
Scientific machine learning (SciML) has emerged as a versatile approach to address complex computational science and engineering problems. Within this field, physics-informed neura…