<ul><li>Classical methods for solving differential equation problems require specifying initial and/or boundary conditions for a unique solution.</li><li>A new study suggests using data-driven and machine learning approaches to infer well-posedness features of differential equation problems.</li><li>These approaches aim to address situations where existence and uniqueness theorems are not known.</li><li>By combining data assimilation and operator learning, researchers aim to make progress in solving complex differential equation problems.</li></ul>

Data-Driven, ML-assisted Approaches to Problem Well-Posedness

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