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Title: Physics-informed machine learning: open mathematical questions
Abstract: The requirements and constraints to numerically solve physics-informed machine learning problems (PhysML) are notably different from classical machine learning for image and signal processing. We will discuss some of the current mathematical challenges that are impeding the wider adoption of these numerical methods in engineering. We will also discuss some of the challenges and research opportunities for the specific case of numerical optimizers in PhysML.